Replication Between Virtual Storage Systems

ABSTRACT

Replication between virtual storage systems includes: constructing a virtual storage system in which the one or more virtual storage devices are coupled to each of one or more virtual storage controllers and replicating a dataset from the virtual storage system to another virtual storage system, where at least one of the virtual storage systems is an on-premises virtual storage system utilizing on-premises physical storage resources.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 3D sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 3E sets forth a flow chart illustrating an example method forservicing I/O operations directed to a dataset that is synchronizedacross a plurality of storage systems according to some embodiments ofthe present disclosure.

FIG. 4 sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 5 sets forth an example of an additional cloud-based storage systemin accordance with some embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an example method ofservicing I/O operations in a cloud-based storage system.

FIG. 7 sets forth a flow chart illustrating an example method ofservicing I/O operations in a cloud-based storage system.

FIG. 8 sets forth a flow chart illustrating an additional example methodof servicing I/O operations in a cloud-based storage system.

FIG. 9 sets forth a flow chart illustrating an additional example methodof servicing I/O operations in a cloud-based storage system.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod of servicing I/O operations in a cloud-based storage system.

FIG. 11 sets forth a flow chart illustrating an additional examplemethod of servicing I/O operations in a cloud-based storage system.

FIG. 12 illustrates an example virtual storage system architecture inaccordance with some embodiments of the present disclosure.

FIG. 13 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 14 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 15 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 16 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 17 sets forth a flow chart illustrating an additional examplemethod of servicing I/O operations in a virtual storage system.

FIG. 18 sets forth a flow chart illustrating an additional examplemethod of servicing I/O operations in a virtual storage system.

FIG. 19 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 20 illustrates an additional example virtual storage systemarchitecture in accordance with some embodiments of the presentdisclosure.

FIG. 21 sets forth a flow chart illustrating an example method ofcreating a virtual storage system.

FIG. 22 sets forth a flow chart illustrating an additional examplemethod of creating a virtual storage system.

FIG. 23 sets forth a flow chart illustrating an additional examplemethod of creating a virtual storage system.

FIG. 24 sets forth a flow chart illustrating an additional examplemethod of creating a virtual storage system.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for virtual storage systemarchitectures in accordance with embodiments of the present disclosureare described with reference to the accompanying drawings, beginningwith FIG. 1A. FIG. 1A illustrates an example system for data storage, inaccordance with some implementations. System 100 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 100 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (PCP) flash storage device 118 with separately addressablefast write storage. System 117 may include a storage controller 119. Inone embodiment, storage controller 119A-D may be a CPU, ASIC, FPGA, orany other circuitry that may implement control structures necessaryaccording to the present disclosure. In one embodiment, system 117includes flash memory devices (e.g., including flash memory devices 120a-n), operatively coupled to various channels of the storage devicecontroller 119. Flash memory devices 120 a-n, may be presented to thecontroller 119A-D as an addressable collection of Flash pages, eraseblocks, and/or control elements sufficient to allow the storage devicecontroller 119A-D to program and retrieve various aspects of the Flash.In one embodiment, storage device controller 119A-D may performoperations on flash memory devices 120 a-n including storing andretrieving data content of pages, arranging and erasing any blocks,tracking statistics related to the use and reuse of Flash memory pages,erase blocks, and cells, tracking and predicting error codes and faultswithin the Flash memory, controlling voltage levels associated withprogramming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS'environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premisewith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagearray 306 and remote, cloud-based storage that is utilized by thestorage array 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, hardware orsoftware upgrades, workflow migrations, or other actions that mayimprove the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The storage resources 308 depicted in FIG. 3A may include various formsof storage-class memory (‘SCM’). SCM may effectively treat fast,non-volatile memory (e.g., NAND flash) as an extension of DRAM such thatan entire dataset may be treated as an in-memory dataset that residesentirely in DRAM. SCM may include non-volatile media such as, forexample, NAND flash. Such NAND flash may be accessed utilizing NVMe thatcan use the PCIe bus as its transport, providing for relatively lowaccess latencies compared to older protocols. In fact, the networkprotocols used for SSDs in all-flash arrays can include NVMe usingEthernet (ROCE, NVME TCP), Fibre Channel (NVMe FC), InfiniBand (iWARP),and others that make it possible to treat fast, non-volatile memory asan extension of DRAM. In view of the fact that DRAM is oftenbyte-addressable and fast, non-volatile memory such as NAND flash isblock-addressable, a controller software/hardware stack may be needed toconvert the block data to the bytes that are stored in the media.Examples of media and software that may be used as SCM can include, forexample, 3D XPoint, Intel Memory Drive Technology, Samsung's Z-SSD, andothers.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniBand (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the presence of such software resources 314may provide for an improved user experience of the storage system 306,an expansion of functionality supported by the storage system 306, andmany other benefits. Consider the specific example of the softwareresources 314 carrying out data backup techniques through which datastored in the storage system may be copied and stored in a distinctlocation to avoid data loss in the event of equipment failure or someother form of catastrophe. In such an example, the systems describedherein may more reliably (and with less burden placed on the user)perform backup operations relative to interactive backup managementsystems that require high degrees of user interactivity, offer lessrobust automation and feature sets, and so on.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers. Furthermore, AI may impact a wide variety of industries andsectors. For example, AI solutions may be used in healthcare to takeclinical notes, patient files, research data, and other inputs togenerate potential treatment options for doctors to explore. Likewise,AI solutions may be used by retailers to personalize consumerrecommendations based on a person's digital footprint of behaviors,profile data, or other data.

Training deep neural networks, however, requires both high quality inputdata and large amounts of computation. GPUs are massively parallelprocessors capable of operating on large amounts of data simultaneously.When combined into a multi-GPU cluster, a high throughput pipeline maybe required to feed input data from storage to the compute engines. Deeplearning is more than just constructing and training models. There alsoexists an entire data pipeline that must be designed for the scale,iteration, and experimentation necessary for a data science team tosucceed.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. Distributed deep learning may can beused to significantly accelerate deep learning with distributedcomputing on GPUs (or other form of accelerator or computer programinstruction executor), such that parallelism can be achieved. Inaddition, the output of training machine learning and deep learningmodels, such as a fully trained machine learning model, may be used fora variety of purposes and in conjunction with other tools. For example,trained machine learning models may be used in conjunction with toolslike Core ML to integrate a broad variety of machine learning modeltypes into an application. In fact, trained models may be run throughCore ML converter tools and inserted into a custom application that canbe deployed on compatible devices. The storage systems described abovemay also be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

Readers will further appreciate that the systems described above may bedeployed in a variety of ways to support the democratization of AI, asAI becomes more available for mass consumption. The democratization ofAI may include, for example, the ability to offer AI as aPlatform-as-a-Service, the growth of Artificial general intelligenceofferings, the proliferation of Autonomous level 4 and Autonomous level5 vehicles, the availability of autonomous mobile robots, thedevelopment of conversational AI platforms, and many others. Forexample, the systems described above may be deployed in cloudenvironments, edge environments, or other environments that are usefulin supporting the democratization of AI. As part of the democratizationof AI, a movement may occur from narrow AI that consists of highlyscoped machine learning solutions that target a particular task toartificial general intelligence where the use of machine learning isexpanded to handle a broad range of use cases that could essentiallyperform any intelligent task that a human could perform and could learndynamically, much like a human.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Readers will appreciate that blockchain technologies may impacta wide variety of industries and sectors. For example, blockchaintechnologies may be used in real estate transactions as blockchain basedcontracts whose use can eliminate the need for 3^(rd) parties and enableself-executing actions when conditions are met. Likewise, universalhealth records can be created by aggregating and placing a person'shealth history onto a blockchain ledger for any healthcare provider, orpermissioned health care providers, to access and update.

Readers will appreciate that the usage of blockchains is not limited tofinancial transactions, contracts, and the like. In fact, blockchainsmay be leveraged to enable the decentralized aggregation, ordering,timestamping and archiving of any type of information, includingstructured data, correspondence, documentation, or other data. Throughthe usage of blockchains, participants can provably and permanentlyagree on exactly what data was entered, when and by whom, withoutrelying on a trusted intermediary. For example, SAP's recently launchedblockchain platform, which supports MultiChain and Hyperledger Fabric,targets a broad range of supply chain and other non-financialapplications.

One way to use a blockchain for recording data is to embed each piece ofdata directly inside a transaction. Every blockchain transaction may bedigitally signed by one or more parties, replicated to a plurality ofnodes, ordered and timestamped by the chain's consensus algorithm, andstored permanently in a tamper-proof way. Any data within thetransaction will therefore be stored identically but independently byevery node, along with a proof of who wrote it and when. The chain'susers are able to retrieve this information at any future time. Thistype of storage may be referred to as on-chain storage. On-chain storagemay not be particularly practical, however, when attempting to store avery large dataset. As such, in accordance with embodiments of thepresent disclosure, blockchains and the storage systems described hereinmay be leveraged to support on-chain storage of data as well asoff-chain storage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Each hash may serve asa commitment to its input data, with the data itself being storedoutside of the blockchain. Readers will appreciate that any blockchainparticipant that needs an off-chain piece of data cannot reproduce thedata from its hash, but if the data can be retrieved in some other way,then the on-chain hash serves to confirm who created it and when. Justlike regular on-chain data, the hash may be embedded inside a digitallysigned transaction, which was included in the chain by consensus.

Readers will appreciate that, in other embodiments, alternatives toblockchains may be used to facilitate the decentralized storage ofinformation. For example, one alternative to a blockchain that may beused is a blockweave. While conventional blockchains store everytransaction to achieve validation, a blockweave permits securedecentralization without the usage of the entire chain, thereby enablinglow cost on-chain storage of data. Such blockweaves may utilize aconsensus mechanism that is based on proof of access (PoA) and proof ofwork (PoW). While typical PoW systems only depend on the previous blockin order to generate each successive block, the PoA algorithm mayincorporate data from a randomly chosen previous block. Combined withthe blockweave data structure, miners do not need to store all blocks(forming a blockchain), but rather can store any previous blocks forminga weave of blocks (a blockweave). This enables increased levels ofscalability, speed and low-cost and reduces the cost of data storage inpart because miners need not store all blocks, thereby resulting in asubstantial reduction in the amount of electricity that is consumedduring the mining process because, as the network expands, electricityconsumption decreases because a blockweave demands less and less hashingpower for consensus as data is added to the system. Furthermore,blockweaves may be deployed on a decentralized storage network in whichincentives are created to encourage rapid data sharing. Suchdecentralized storage networks may also make use of blockshadowingtechniques, where nodes only send a minimal block “shadow” to othernodes that allows peers to reconstruct a full block, instead oftransmitting the full block itself.

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In memory computing involves the storage of information inRAM that is distributed across a cluster of computers. In-memorycomputing helps business customers, including retailers, banks andutilities, to quickly detect patterns, analyze massive data volumes onthe fly, and perform their operations quickly. Readers will appreciatethat the storage systems described above, especially those that areconfigurable with customizable amounts of processing resources, storageresources, and memory resources (e.g., those systems in which bladesthat contain configurable amounts of each type of resource), may beconfigured in a way so as to provide an infrastructure that can supportin-memory computing. Likewise, the storage systems described above mayinclude component parts (e.g., NVDIMMs, 3D crosspoint storage thatprovide fast random access memory that is persistent) that can actuallyprovide for an improved in-memory computing environment as compared toin-memory computing environments that rely on RAM distributed acrossdedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services. Such platforms mayseamlessly collect, organize, secure, and analyze data across anenterprise, as well as simplify hybrid data management, unified datagovernance and integration, data science and business analytics with asingle solution.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade(s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered byhigh-performance analytics systems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others. The storage systems describedabove may also, either alone or in combination with other computingenvironments, be used to deliver a wide range of transparently immersiveexperiences where technology can introduce transparency between people,businesses, and things. Such transparently immersive experiences may bedelivered as augmented reality technologies, connected homes, virtualreality technologies, brain—computer interfaces, human augmentationtechnologies, nanotube electronics, volumetric displays, 4D printingtechnologies, or others. The storage systems described above may also,either alone or in combination with other computing environments, beused to support a wide variety of digital platforms. Such digitalplatforms can include, for example, 5G wireless systems and platforms,digital twin platforms, edge computing platforms, IoT platforms, quantumcomputing platforms, serverless PaaS, software-defined security,neuromorphic computing platforms, and so on.

Readers will appreciate that some transparently immersive experiencesmay involve the use of digital twins of various “things” such as people,places, processes, systems, and so on. Such digital twins and otherimmersive technologies can alter the way that humans interact withtechnology, as conversational platforms, augmented reality, virtualreality and mixed reality provide a more natural and immersiveinteraction with the digital world. In fact, digital twins may be linkedwith the real-world, perhaps even in real-time, to understand the stateof a thing or system, respond to changes, and so on. Because digitaltwins consolidate massive amounts of information on individual assetsand groups of assets (even possibly providing control of those assets),digital twins may communicate with each other to digital factory modelsof multiple linked digital twins.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload. Furthermore,application monitoring and visibility tools may be deployed to moveapplication workloads around different clouds, identify performanceissues, and perform other tasks. In addition, security and compliancetools may be deployed for to ensure compliance with securityrequirements, government regulations, and so on. Such a multi-cloudenvironment may also include tools for application delivery and smartworkload management to ensure efficient application delivery and helpdirect workloads across the distributed and heterogeneousinfrastructure, as well as tools that ease the deployment andmaintenance of packaged and custom applications in the cloud and enableportability amongst clouds. The multi-cloud environment may similarlyinclude tools for data portability.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Such crypto-anchors may take manyforms including, for example, as edible ink, as a mobile sensor, as amicrochip, and others. Similarly, as part of a suite of tools to securedata stored on the storage system, the storage systems described abovemay implement various encryption technologies and schemes, includinglattice cryptography. Lattice cryptography can involve constructions ofcryptographic primitives that involve lattices, either in theconstruction itself or in the security proof. Unlike public-key schemessuch as the RSA, Diffie-Hellman or Elliptic-Curve cryptosystems, whichare easily attacked by a quantum computer, some lattice-basedconstructions appear to be resistant to attack by both classical andquantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers. Through the use of a parallel file system, file contents may bedistributed over a plurality of storage servers using striping andmetadata may be distributed over a plurality of metadata servers on adirectory level, with each server storing a part of the complete filesystem tree. Readers will appreciate that in some embodiments, thestorage servers and metadata servers may run in userspace on top of anexisting local file system. Furthermore, dedicated hardware is notrequired for client services, the metadata servers, or the hardwareservers as metadata servers, storage servers, and even the clientservices may be run on the same machines.

Readers will appreciate that, in part due to the emergence of many ofthe technologies discussed above including mobile devices, cloudservices, social networks, big data analytics, and so on, an informationtechnology platform may be needed to integrate all of these technologiesand drive new business opportunities by quickly deliveringrevenue-generating products, services, and experiences—rather thanmerely providing the technology to automate internal business processes.Information technology organizations may need to balance resources andinvestments needed to keep core legacy systems up and running while alsointegrating technologies to build an information technology platformthat can provide the speed and flexibility in areas such as, forexample, exploiting big data, managing unstructured data, and workingwith cloud applications and services. One possible embodiment of such aninformation technology platform is a composable infrastructure thatincludes fluid resource pools, such as many of the systems describedabove that, can meet the changing needs of applications by allowing forthe composition and recomposition of blocks of disaggregated compute,storage, and fabric infrastructure. Such a composable infrastructure canalso include a single management interface to eliminate complexity and aunified API to discover, search, inventory, configure, provision,update, and diagnose the composable infrastructure.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm, aclustering and scheduling tool for Docker containers that enables ITadministrators and developers to establish and manage a cluster ofDocker nodes as a single virtual system. Likewise, containerizedapplications may be managed through the use of Kubernetes, acontainer-orchestration system for automating deployment, scaling andmanagement of containerized applications. Kubernetes may execute on topof operating systems such as, for example, Red Hat Enterprise Linux,Ubuntu Server, SUSE Linux Enterprise Servers, and others. In suchexamples, a master node may assign tasks to worker/minion nodes.Kubernetes can include a set of components (e.g., kubelet, kube-proxy,cAdvisor) that manage individual nodes as a well as a set of components(e.g., etcd, API server, Scheduler, Control Manager) that form a controlplane. Various controllers (e.g., Replication Controller, DaemonSetController) can drive the state of a Kubernetes cluster by managing aset of pods that includes one or more containers that are deployed on asingle node. Containerized applications may be used to facilitate aserverless, cloud native computing deployment and management model forsoftware applications. In support of a serverless, cloud nativecomputing deployment and management model for software applications,containers may be used as part of an event handling mechanisms (e.g.,AWS Lambdas) such that various events cause a containerized applicationto be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better. MEC technology is designed to be implemented at thecellular base stations or other edge nodes, and enables flexible andrapid deployment of new applications and services for customers. MEC mayalso allow cellular operators to open their radio access network (‘RAN’)to authorized third-parties, such as application developers and contentprovider. Furthermore, edge computing and micro data centers maysubstantially reduce the cost of smartphones that work with the 5Gnetwork because customer may not need devices with such intensiveprocessing power and the expensive requisite components.

Readers will appreciate that 5G networks may generate more data thanprevious network generations, especially in view of the fact that thehigh network bandwidth offered by 5G networks may cause the 5G networksto handle amounts and types of data (e.g., sensor data from self-drivingcars, data generated by AR/VR technologies) that weren't as feasible forprevious generation networks. In such examples, the scalability offeredby the systems described above may be very valuable as the amount ofdata increases, adoption of emerging technologies increase, and so on.

For further explanation, FIG. 3C illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3C, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3C, the components illustrated in FIG. 3C are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3C willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3D sets forth a block diagram illustratinga plurality of storage systems (311-402, 311-404, 311-406) that supporta pod according to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage systems (311-402, 311-404, 311-406)depicted in FIG. 3D may be similar to the storage systems describedabove with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIG. 3A-3B, or anycombination thereof. In fact, the storage systems (311-402, 311-404,311-406) depicted in FIG. 3D may include the same, fewer, or additionalcomponents as the storage systems described above.

In the example depicted in FIG. 3D, each of the storage systems(311-402, 311-404, 311-406) is depicted as having at least one computerprocessor (311-408, 311-410, 311-412), computer memory (311-414,311-416, 311-418), and computer storage (311-420, 311-422, 311-424).Although in some embodiments the computer memory (311-414, 311-416,311-418) and the computer storage (311-420, 311-422, 311-424) may bepart of the same hardware devices, in other embodiments the computermemory (311-414, 311-416, 311-418) and the computer storage (311-420,311-422, 311-424) may be part of different hardware devices. Thedistinction between the computer memory (311-414, 311-416, 311-418) andthe computer storage (311-420, 311-422, 311-424) in this particularexample may be that the computer memory (311-414, 311-416, 311-418) isphysically proximate to the computer processors (311-408, 311-410,311-412) and may store computer program instructions that are executedby the computer processors (311-408, 311-410, 311-412), while thecomputer storage (311-420, 311-422, 311-424) is embodied as non-volatilestorage for storing user data, metadata describing the user data, and soon. Referring to the example above in FIG. 1A, for example, the computerprocessors (311-408, 311-410, 311-412) and computer memory (311-414,311-416, 311-418) for a particular storage system (311-402, 311-404,311-406) may reside within one of more of the controllers (110A-110D)while the attached storage devices (171A-171F) may serve as the computerstorage (311-420, 311-422, 311-424) within a particular storage system(311-402, 311-404, 311-406).

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may attach to one or more pods (311-430,311-432) according to some embodiments of the present disclosure. Eachof the pods (311-430, 311-432) depicted in FIG. 3D can include a dataset(311-426, 311-428). For example, a first pod (311-430) that threestorage systems (311-402, 311-404, 311-406) have attached to includes afirst dataset (311-426) while a second pod (311-432) that two storagesystems (311-404, 311-406) have attached to includes a second dataset(311-428). In such an example, when a particular storage system attachesto a pod, the pod's dataset is copied to the particular storage systemand then kept up to date as the dataset is modified. Storage systems canbe removed from a pod, resulting in the dataset being no longer kept upto date on the removed storage system. In the example depicted in FIG.3D, any storage system which is active for a pod (it is an up-to-date,operating, non-faulted member of a non-faulted pod) can receive andprocess requests to modify or read the pod's dataset.

In the example depicted in FIG. 3D, each pod (311-430, 311-432) may alsoinclude a set of managed objects and management operations, as well as aset of access operations to modify or read the dataset (311-426,311-428) that is associated with the particular pod (311-430, 311-432).In such an example, the management operations may modify or querymanaged objects equivalently through any of the storage systems.Likewise, access operations to read or modify the dataset may operateequivalently through any of the storage systems. In such an example,while each storage system stores a separate copy of the dataset as aproper subset of the datasets stored and advertised for use by thestorage system, the operations to modify managed objects or the datasetperformed and completed through any one storage system are reflected insubsequent management objects to query the pod or subsequent accessoperations to read the dataset.

Readers will appreciate that pods may implement more capabilities thanjust a clustered synchronously replicated dataset. For example, pods canbe used to implement tenants, whereby datasets are in some way securelyisolated from each other. Pods can also be used to implement virtualarrays or virtual storage systems where each pod is presented as aunique storage entity on a network (e.g., a Storage Area Network, orInternet Protocol network) with separate addresses. In the case of amulti-storage-system pod implementing a virtual storage system, allphysical storage systems associated with the pod may present themselvesas in some way the same storage system (e.g., as if the multiplephysical storage systems were no different than multiple network portsinto a single storage system).

Readers will appreciate that pods may also be units of administration,representing a collection of volumes, file systems, object/analyticstores, snapshots, and other administrative entities, where makingadministrative changes (e.g., name changes, property changes, managingexports or permissions for some part of the pod's dataset), on any onestorage system is automatically reflected to all active storage systemsassociated with the pod. In addition, pods could also be units of datacollection and data analysis, where performance and capacity metrics arepresented in ways that aggregate across all active storage systems forthe pod, or that call out data collection and analysis separately foreach pod, or perhaps presenting each attached storage system'scontribution to the incoming content and performance for each a pod.

One model for pod membership may be defined as a list of storagesystems, and a subset of that list where storage systems are consideredto be in-sync for the pod. A storage system may be considered to bein-sync for a pod if it is at least within a recovery of havingidentical idle content for the last written copy of the datasetassociated with the pod. Idle content is the content after anyin-progress modifications have completed with no processing of newmodifications. Sometimes this is referred to as “crash recoverable”consistency. Recovery of a pod carries out the process of reconcilingdifferences in applying concurrent updates to in-sync storage systems inthe pod. Recovery can resolve any inconsistencies between storagesystems in the completion of concurrent modifications that had beenrequested to various members of the pod but that were not signaled toany requestor as having completed successfully. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

Each storage system member of a pod may have its own copy of themembership, including which storage systems it last knew were in-sync,and which storage systems it last knew comprised the entire set of podmembers. To be online for a pod, a storage system must consider itselfto be in-sync for the pod and must be communicating with all otherstorage systems it considers to be in-sync for the pod. If a storagesystem can't be certain that it is in-sync and communicating with allother storage systems that are in-sync, then it must stop processing newincoming requests for the pod (or must complete them with an error orexception) until it can be certain that it is in-sync and communicatingwith all other storage systems that are in-sync. A first storage systemmay conclude that a second paired storage system should be detached,which will allow the first storage system to continue since it is nowin-sync with all storage systems now in the list. But, the secondstorage system must be prevented from concluding, alternatively, thatthe first storage system should be detached and with the second storagesystem continuing operation. This would result in a “split brain”condition that can lead to irreconcilable datasets, dataset corruption,or application corruption, among other dangers.

The situation of needing to determine how to proceed when notcommunicating with paired storage systems can arise while a storagesystem is running normally and then notices lost communications, whileit is currently recovering from some previous fault, while it isrebooting or resuming from a temporary power loss or recoveredcommunication outage, while it is switching operations from one set ofstorage system controller to another set for whatever reason, or duringor after any combination of these or other kinds of events. In fact, anytime a storage system that is associated with a pod can't communicatewith all known non-detached members, the storage system can either waitbriefly until communications can be established, go offline and continuewaiting, or it can determine through some means that it is safe todetach the non-communicating storage system without risk of incurring asplit brain due to the non-communicating storage system concluding thealternative view, and then continue. If a safe detach can happen quicklyenough, the storage system can remain online for the pod with littlemore than a short delay and with no resulting application outages forapplications that can issue requests to the remaining online storagesystems.

One example of this situation is when a storage system may know that itis out-of-date. That can happen, for example, when a first storagesystem is first added to a pod that is already associated with one ormore storage systems, or when a first storage system reconnects toanother storage system and finds that the other storage system hadalready marked the first storage system as detached. In this case, thisfirst storage system will simply wait until it connects to some otherset of storage systems that are in-sync for the pod.

This model demands some degree of consideration for how storage systemsare added to or removed from pods or from the in-sync pod members list.Since each storage system will have its own copy of the list, and sincetwo independent storage systems can't update their local copy at exactlythe same time, and since the local copy is all that is available on areboot or in various fault scenarios, care must be taken to ensure thattransient inconsistencies don't cause problems. For example, if onestorage system is in-sync for a pod and a second storage system isadded, then if the second storage system is updated to list both storagesystems as in-sync first, then if there is a fault and a restart of bothstorage systems, the second might startup and wait to connect to thefirst storage system while the first might be unaware that it should orcould wait for the second storage system. If the second storage systemthen responds to an inability to connect with the first storage systemby going through a process to detach it, then it might succeed incompleting a process that the first storage system is unaware of,resulting in a split brain. As such, it may be necessary to ensure thatstorage systems won't disagree inappropriately on whether they might optto go through a detach process if they aren't communicating.

One way to ensure that storage systems won't disagree inappropriately onwhether they might opt to go through a detach process if they aren'tcommunicating is to ensure that when adding a new storage system to thein-sync member list for a pod, the new storage system first stores thatit is a detached member (and perhaps that it is being added as anin-sync member). Then, the existing in-sync storage systems can locallystore that the new storage system is an in-sync pod member before thenew storage system locally stores that same fact. If there is a set ofreboots or network outages prior to the new storage system storing itsin-sync status, then the original storage systems may detach the newstorage system due to non-communication, but the new storage system willwait. A reverse version of this change might be needed for removing acommunicating storage system from a pod: first the storage system beingremoved stores that it is no longer in-sync, then the storage systemsthat will remain store that the storage system being removed is nolonger in-sync, then all storage systems delete the storage system beingremoved from their pod membership lists. Depending on theimplementation, an intermediate persisted detached state may not benecessary. Whether or not care is required in local copies of membershiplists may depend on the model storage systems use for monitoring eachother or for validating their membership. If a consensus model is usedfor both, or if an external system (or an external distributed orclustered system) is used to store and validate pod membership, theninconsistencies in locally stored membership lists may not matter.

When communications fail or one or several storage systems in a podfail, or when a storage system starts up (or fails over to a secondarycontroller) and can't communicate with paired storage systems for a pod,and it is time for one or more storage systems to decide to detach oneor more paired storage systems, some algorithm or mechanism must beemployed to decide that it is safe to do so and to follow through on thedetach. One means of resolving detaches is use a majority (or quorum)model for membership. With three storage systems, as long as two arecommunicating, they can agree to detach a third storage system thatisn't communicating, but that third storage system cannot by itselfchoose to detach either of the other two. Confusion can arise whenstorage system communication is inconsistent. For example, storagesystem A might be communicating with storage system B but not C, whilestorage system B might be communicating with both A and C. So, A and Bcould detach C, or B and C could detach A, but more communicationbetween pod members may be needed to figure this out.

Care needs to be taken in a quorum membership model when adding andremoving storage systems. For example, if a fourth storage system isadded, then a “majority” of storage systems is at that point three. Thetransition from three storage systems (with two required for majority)to a pod including a fourth storage system (with three required formajority) may require something similar to the model describedpreviously for carefully adding a storage system to the in-sync list.For example, the fourth storage system might start in an attaching statebut not yet attached where it would never instigate a vote over quorum.Once in that state, the original three pod members could each be updatedto be aware of the fourth member and the new requirement for a threestorage system majority to detach a fourth. Removing a storage systemfrom a pod might similarly move that storage system to a locally stored“detaching” state before updating other pod members. A variant schemefor this is to use a distributed consensus mechanism such as PAXOS orRAFT to implement any membership changes or to process detach requests.

Another means of managing membership transitions is to use an externalsystem that is outside of the storage systems themselves to handle podmembership. In order to become online for a pod, a storage system mustfirst contact the external pod membership system to verify that it isin-sync for the pod. Any storage system that is online for a pod shouldthen remain in communication with the pod membership system and shouldwait or go offline if it loses communication. An external pod membershipmanager could be implemented as a highly available cluster using variouscluster tools, such as Oracle RAC, Linux HA, VERITAS Cluster Server,IBM's HACMP, or others. An external pod membership manager could alsouse distributed configuration tools such as Etcd or Zookeeper, or areliable distributed database such as Amazon's DynamoDB.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may receive a request to read a portion ofthe dataset (311-426, 311-428) and process the request to read theportion of the dataset locally according to some embodiments of thepresent disclosure. Readers will appreciate that although requests tomodify (e.g., a write operation) the dataset (311-426, 311-428) requirecoordination between the storage systems (311-402, 311-404, 311-406) ina pod, as the dataset (311-426, 311-428) should be consistent across allstorage systems (311-402, 311-404, 311-406) in a pod, responding to arequest to read a portion of the dataset (311-426, 311-428) does notrequire similar coordination between the storage systems (311-402,311-404, 311-406). As such, a particular storage system that receives aread request may service the read request locally by reading a portionof the dataset (311-426, 311-428) that is stored within the storagesystem's storage devices, with no synchronous communication with otherstorage systems in the pod. Read requests received by one storage systemfor a replicated dataset in a replicated cluster are expected to avoidany communication in the vast majority of cases, at least when receivedby a storage system that is running within a cluster that is alsorunning nominally. Such reads should normally be processed simply byreading from the local copy of a clustered dataset with no furtherinteraction required with other storage systems in the cluster

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations should not yield a result as if they happened in oneorder on one storage system and a different order on another storagesystem in the replicated cluster.

Furthermore, read requests can be made time order consistent. Forexample, if one read request is received on a replicated cluster andcompleted and that read is then followed by another read request to anoverlapping address range which is received by the replicated clusterand where one or both reads in any way overlap in time and volumeaddress range with a modification request received by the replicatedcluster (whether any of the reads or the modification are received bythe same storage system or a different storage system in the replicatedcluster), then if the first read reflects the result of the update thenthe second read should also reflect the results of that update, ratherthan possibly returning data that preceded the update. If the first readdoes not reflect the update, then the second read can either reflect theupdate or not. This ensures that between two read requests “time” for adata segment cannot roll backward.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also detect a disruption in datacommunications with one or more of the other storage systems anddetermine whether to the particular storage system should remain in thepod. A disruption in data communications with one or more of the otherstorage systems may occur for a variety of reasons. For example, adisruption in data communications with one or more of the other storagesystems may occur because one of the storage systems has failed, becausea network interconnect has failed, or for some other reason. Animportant aspect of synchronous replicated clustering is ensuring thatany fault handling doesn't result in unrecoverable inconsistencies, orany inconsistency in responses. For example, if a network fails betweentwo storage systems, at most one of the storage systems can continueprocessing newly incoming I/O requests for a pod. And, if one storagesystem continues processing, the other storage system can't process anynew requests to completion, including read requests.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also determine whether the particularstorage system should remain in the pod in response to detecting adisruption in data communications with one or more of the other storagesystems. As mentioned above, to be ‘online’ as part of a pod, a storagesystem must consider itself to be in-sync for the pod and must becommunicating with all other storage systems it considers to be in-syncfor the pod. If a storage system can't be certain that it is in-sync andcommunicating with all other storage systems that are in-sync, then itmay stop processing new incoming requests to access the dataset(311-426, 311-428). As such, the storage system may determine whether tothe particular storage system should remain online as part of the pod,for example, by determining whether it can communicate with all otherstorage systems it considers to be in-sync for the pod (e.g., via one ormore test messages), by determining whether the all other storagesystems it considers to be in-sync for the pod also consider the storagesystem to be attached to the pod, through a combination of both stepswhere the particular storage system must confirm that it can communicatewith all other storage systems it considers to be in-sync for the podand that all other storage systems it considers to be in-sync for thepod also consider the storage system to be attached to the pod, orthrough some other mechanism.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also keep the dataset on the particularstorage system accessible for management and dataset operations inresponse to determining that the particular storage system should remainin the pod. The storage system may keep the dataset (311-426, 311-428)on the particular storage system accessible for management and datasetoperations, for example, by accepting requests to access the version ofthe dataset (311-426, 311-428) that is stored on the storage system andprocessing such requests, by accepting and processing managementoperations associated with the dataset (311-426, 311-428) that areissued by a host or authorized administrator, by accepting andprocessing management operations associated with the dataset (311-426,311-428) that are issued by one of the other storage systems, or in someother way.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may, however, make the dataset on theparticular storage system inaccessible for management and datasetoperations in response to determining that the particular storage systemshould not remain in the pod. The storage system may make the dataset(311-426, 311-428) on the particular storage system inaccessible formanagement and dataset operations, for example, by rejecting requests toaccess the version of the dataset (311-426, 311-428) that is stored onthe storage system, by rejecting management operations associated withthe dataset (311-426, 311-428) that are issued by a host or otherauthorized administrator, by rejecting management operations associatedwith the dataset (311-426, 311-428) that are issued by one of the otherstorage systems in the pod, or in some other way.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also detect that the disruption in datacommunications with one or more of the other storage systems has beenrepaired and make the dataset on the particular storage systemaccessible for management and dataset operations. The storage system maydetect that the disruption in data communications with one or more ofthe other storage systems has been repaired, for example, by receiving amessage from the one or more of the other storage systems. In responseto detecting that the disruption in data communications with one or moreof the other storage systems has been repaired, the storage system maymake the dataset (311-426, 311-428) on the particular storage systemaccessible for management and dataset operations once the previouslydetached storage system has been resynchronized with the storage systemsthat remained attached to the pod.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also go offline from the pod such thatthe particular storage system no longer allows management and datasetoperations. The depicted storage systems (311-402, 311-404, 311-406) maygo offline from the pod such that the particular storage system nolonger allows management and dataset operations for a variety ofreasons. For example, the depicted storage systems (311-402, 311-404,311-406) may also go offline from the pod due to some fault with thestorage system itself, because an update or some other maintenance isoccurring on the storage system, due to communications faults, or formany other reasons. In such an example, the depicted storage systems(311-402, 311-404, 311-406) may subsequently update the dataset on theparticular storage system to include all updates to the dataset sincethe particular storage system went offline and go back online with thepod such that the particular storage system allows management anddataset operations, as will be described in greater detail in theresynchronization sections included below.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also identifying a target storage systemfor asynchronously receiving the dataset, where the target storagesystem is not one of the plurality of storage systems across which thedataset is synchronously replicated. Such a target storage system mayrepresent, for example, a backup storage system, as some storage systemthat makes use of the synchronously replicated dataset, and so on. Infact, synchronous replication can be leveraged to distribute copies of adataset closer to some rack of servers, for better local readperformance. One such case is smaller top-of-rack storage systemssymmetrically replicated to larger storage systems that are centrallylocated in the data center or campus and where those larger storagesystems are more carefully managed for reliability or are connected toexternal networks for asynchronous replication or backup services.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also identify a portion of the datasetthat is not being asynchronously replicated to the target storage systemby any of the other storages systems and asynchronously replicate, tothe target storage system, the portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems, wherein the two or more storage systemscollectively replicate the entire dataset to the target storage system.In such a way, the work associated with asynchronously replicating aparticular dataset may be split amongst the members of a pod, such thateach storage system in a pod is only responsible for asynchronouslyreplicating a subset of a dataset to the target storage system.

In the example depicted in FIG. 3D, the depicted storage systems(311-402, 311-404, 311-406) may also detach from the pod, such that theparticular storage system that detaches from the pod is no longerincluded in the set of storage systems across which the dataset issynchronously replicated. For example, if storage system (311-404) inFIG. 3D detached from the pod (311-430) illustrated in FIG. 3D, the pod(311-430) would only include storage systems (311-402, 311-406) as thestorage systems across which the dataset (311-426) that is included inthe pod (311-430) would be synchronously replicated across. In such anexample, detaching the storage system from the pod could also includeremoving the dataset from the particular storage system that detachedfrom the pod. Continuing with the example where the storage system(311-404) in FIG. 3D detached from the pod (311-430) illustrated in FIG.3D, the dataset (311-426) that is included in the pod (311-430) could bedeleted or otherwise removed from the storage system (311-404).

Readers will appreciate that there are a number of unique administrativecapabilities enabled by the pod model that can further be supported.Also, the pod model itself introduces some issues that can be addressedby an implementation. For example, when a storage system is offline fora pod, but is otherwise running, such as because an interconnect failedand another storage system for the pod won out in mediation, there maystill be a desire or need to access the offline pod's dataset on theoffline storage system. One solution may be simply to enable the pod insome detached mode and allow the dataset to be accessed. However, thatsolution can be dangerous and that solution can cause the pod's metadataand data to be much more difficult to reconcile when the storage systemsdo regain communication. Furthermore, there could still be a separatepath for hosts to access the offline storage system as well as the stillonline storage systems. In that case, a host might issue I/O to bothstorage systems even though they are no longer being kept in sync,because the host sees target ports reporting volumes with the sameidentifiers and the host I/O drivers presume it sees additional paths tothe same volume. This can result in fairly damaging data corruption asreads and writes issued to both storage systems are no longer consistenteven though the host presumes they are. As a variant of this case, in aclustered application, such as a shared storage clustered database, theclustered application running on one host might be reading or writing toone storage system and the same clustered application running on anotherhost might be reading or writing to the “detached” storage system, yetthe two instances of the clustered application are communicating betweeneach other on the presumption that the dataset they each see is entirelyconsistent for completed writes. Since they aren't consistent, thatpresumption is violated and the application's dataset (e.g., thedatabase) can quickly end up being corrupted.

One way to solve both of these problems is to allow for an offline pod,or perhaps a snapshot of an offline pod, to be copied to a new pod withnew volumes that have sufficiently new identities that host I/O driversand clustered applications won't confuse the copied volumes as being thesame as the still online volumes on another storage system. Since eachpod maintains a complete copy of the dataset, which is crash consistentbut perhaps slightly different from the copy of the pod dataset onanother storage system, and since each pod has an independent copy ofall data and metadata needed to operate on the pod content, it is astraightforward problem to make a virtual copy of some or all volumes orsnapshots in the pod to new volumes in a new pod. In a logical extentgraph implementation, for example, all that is needed is to define newvolumes in a new pod which reference logical extent graphs from thecopied pod associated with the pod's volumes or snapshots, and with thelogical extent graphs being marked as copy on write. The new volumesshould be treated as new volumes, similarly to how volume snapshotscopied to a new volume might be implemented. Volumes may have the sameadministrative name, though within a new pod namespace. But, they shouldhave different underlying identifiers, and differing logical unitidentifiers from the original volumes.

In some cases it may be possible to use virtual network isolationtechniques (for example, by creating a virtual LAN in the case of IPnetworks or a virtual SAN in the case of fiber channel networks) in sucha way that isolation of volumes presented to some interfaces can beassured to be inaccessible from host network interfaces or host SCSIinitiator ports that might also see the original volumes. In such cases,it may be safe to provide the copies of volumes with the same SCSI orother storage identifiers as the original volumes. This could be used,for example, in cases where the applications expect to see a particularset of storage identifiers in order to function without an undue burdenin reconfiguration.

Some of the techniques described herein could also be used outside of anactive fault context to test readiness for handling faults. Readinesstesting (sometimes referred to as “fire drills”) is commonly requiredfor disaster recovery configurations, where frequent and repeatedtesting is considered a necessity to ensure that most or all aspects ofa disaster recovery plan are correct and account for any recent changesto applications, datasets, or changes in equipment. Readiness testingshould be non-disruptive to current production operations, includingreplication. In many cases the real operations can't actually be invokedon the active configuration, but a good way to get close is to usestorage operations to make copies of production datasets, and thenperhaps couple that with the use of virtual networking, to create anisolated environment containing all data that is believed necessary forthe important applications that must be brought up successfully in casesof disasters. Making such a copy of a synchronously replicated (or evenan asynchronously replicated) dataset available within a site (orcollection of sites) that is expected to perform a disaster recoveryreadiness test procedure and then starting the important applications onthat dataset to ensure that it can startup and function is a great tool,since it helps ensure that no important parts of the applicationdatasets were left out in the disaster recovery plan. If necessary, andpractical, this could be coupled with virtual isolated networks coupledperhaps with isolated collection of physical or virtual machines, to getas close as possible to a real world disaster recovery takeoverscenario. Virtually copying a pod (or set of pods) to another pod as apoint-in-time image of the pod datasets immediately creates an isolateddataset that contains all the copied elements and that can then beoperated on essentially identically to the originally pods, as well asallowing isolation to a single site (or a few sites) separately from theoriginal pod. Further, these are fast operations and they can be torndown and repeated easily allowing testing to repeated as often as isdesired.

Some enhancements could be made to get further toward perfect disasterrecovery testing. For example, in conjunction with isolated networks,SCSI logical unit identities or other types of identities could becopied into the target pod so that the test servers, virtual machines,and applications see the same identities. Further, the administrativeenvironment of the servers could be configured to respond to requestsfrom a particular virtual set of virtual networks to respond to requestsand operations on the original pod name so scripts don't require use oftest-variants with alternate “test” versions of object names. A furtherenhancement can be used in cases where the host-side serverinfrastructure that will take over in the case of a disaster takeovercan be used during a test. This includes cases where a disaster recoverydata center is completely stocked with alternative server infrastructurethat won't generally be used until directed to do so by a disaster. Italso includes cases where that infrastructure might be used fornon-critical operations (for example, running analytics on productiondata, or simply supporting application development or other functionswhich may be important but can be halted if needed for more criticalfunctions). Specifically, host definitions and configurations and theserver infrastructure that will use them can be set up as they will befor an actual disaster recovery takeover event and tested as part ofdisaster recovery takeover testing, with the tested volumes beingconnected to these host definitions from the virtual pod copy used toprovide a snapshot of the dataset. From the standpoint of the storagesystems involved, then, these host definitions and configurations usedfor testing, and the volume-to-host connection configurations usedduring testing, can be reused when an actual disaster takeover event istriggered, greatly minimizing the configuration differences between thetest configuration and the real configuration that will be used in caseof a disaster recovery takeover.

In some cases it may make sense to move volumes out of a first pod andinto a new second pod including just those volumes. The pod membershipand high availability and recovery characteristics can then be adjustedseparately, and administration of the two resulting pod datasets canthen be isolated from each other. An operation that can be done in onedirection should also be possible in the other direction. At some point,it may make sense to take two pods and merge them into one so that thevolumes in each of the original two pods will now track each other forstorage system membership and high availability and recoverycharacteristics and events. Both operations can be accomplished safelyand with reasonably minimal or no disruption to running applications byrelying on the characteristics suggested for changing mediation orquorum properties for a pod which were discussed in an earlier section.With mediation, for example, a mediator for a pod can be changed using asequence consisting of a step where each storage system in a pod ischanged to depend on both a first mediator and a second mediator andeach is then changed to depend only on the second mediator. If a faultoccurs in the middle of the sequence, some storage systems may depend onboth the first mediator and the second mediator, but in no case willrecovery and fault handling result in some storage systems dependingonly on the first mediator and other storage systems only depending onthe second mediator. Quorum can be handled similarly by temporarilydepending on winning against both a first quorum model and a secondquorum model in order to proceed to recovery. This may result in a veryshort time period where availability of the pod in the face of faultsdepend on additional resources, thus reducing potential availability,but this time period is very short and the reduction in availability isoften very little. With mediation, if the change in mediator parametersis nothing more than the change in the key used for mediation and themediation service used is the same, then the potential reduction inavailability is even less, since it now depends only on two calls to thesame service versus one call to that service, and rather than separatecalls to two separate services.

Readers will note that changing the quorum model may be quite complex.An additional step may be necessary where storage systems willparticipate in the second quorum model but won't depend on winning inthat second quorum model, which is then followed by the step of alsodepending on the second quorum model. This may be necessary to accountfor the fact that if only one system has processed the change to dependon the quorum model, then it will never win quorum since there willnever be a majority. With this model in place for changing the highavailability parameters (mediation relationship, quorum model, takeoverpreferences), we can create a safe procedure for these operations tosplit a pod into two or to join two pods into one. This may requireadding one other capability: linking a second pod to a first pod forhigh availability such that if two pods include compatible highavailability parameters the second pod linked to the first pod candepend on the first pod for determining and instigating detach-relatedprocessing and operations, offline and in-sync states, and recovery andresynchronization actions.

To split a pod into two, which is an operation to move some volumes intoa newly created pod, a distributed operation may be formed that can bedescribed as: form a second pod into which we will move a set of volumeswhich were previously in a first pod, copy the high availabilityparameters from the first pod into the second pod to ensure they arecompatible for linking, and link the second pod to the first pod forhigh availability. This operation may be encoded as messages and shouldbe implemented by each storage system in the pod in such a way that thestorage system ensures that the operation happens completely on thatstorage system or does not happen at all if processing is interrupted bya fault. Once all in-sync storage systems for the two pods haveprocessed this operation, the storage systems can then process asubsequent operation which changes the second pod so that it is nolonger linked to the first pod. As with other changes to highavailability characteristics for a pod, this involves first having eachin-sync storage system change to rely on both the previous model (thatmodel being that high availability is linked to the first pod) and thenew model (that model being its own now independent high availability).In the case of mediation or quorum, this means that storage systemswhich processed this change will first depend on mediation or quorumbeing achieved as appropriate for the first pod and will additionallydepend on a new separate mediation (for example, a new mediation key) orquorum being achieved for the second pod before the second pod canproceed following a fault that required mediation or testing for quorum.As with the previous description of changing quorum models, anintermediate step may set storage systems to participate in quorum forthe second pod before the step where storage systems participate in anddepend on quorum for the second pod. Once all in-sync storage systemshave processed the change to depend on the new parameters for mediationor quorum for both the first pod and the second pod, the split iscomplete.

Joining a second pod into a first pod operates essentially in reverse.First, the second pod must be adjusted to be compatible with the firstpod, by having an identical list of storage systems and by having acompatible high availability model. This may involve some set of stepssuch as those described elsewhere in this paper to add or remove storagesystems or to change mediator and quorum models. Depending onimplementation, it may be necessary only to reach an identical list ofstorage systems. Joining proceeds by processing an operation on eachin-sync storage system to link the second pod to the first pod for highavailability. Each storage system which processes that operation willthen depend on the first pod for high availability and then the secondpod for high availability. Once all in-sync storage systems for thesecond pod have processed that operation, the storage systems will theneach process a subsequent operation to eliminate the link between thesecond pod and the first pod, migrate the volumes from the second podinto the first pod, and delete the second pod. Host or applicationdataset access can be preserved throughout these operations, as long asthe implementation allows proper direction of host or applicationdataset modification or read operations to the volume by identity and aslong as the identity is preserved as appropriate to the storage protocolor storage model (for example, as long as logical unit identifiers forvolumes and use of target ports for accessing volumes are preserved inthe case of SCSI).

Migrating a volume between pods may present issues. If the pods have anidentical set of in-sync membership storage systems, then it may bestraightforward: temporarily suspend operations on the volumes beingmigrated, switch control over operations on those volumes to controllingsoftware and structures for the new pod, and then resume operations.This allows for a seamless migration with continuous uptime forapplications apart from the very brief operation suspension, providednetwork and ports migrate properly between pods. Depending on theimplementation, suspending operations may not even be necessary, or maybe so internal to the system that the suspension of operations has noimpact. Copying volumes between pods with different in-sync membershipsets is more of a problem. If the target pod for the copy has a subsetof in-sync members from the source pod, this isn't much of a problem: amember storage system can be dropped safely enough without having to domore work. But, if the target pod adds in-sync member storage systems tothe volume over the source pod, then the added storage systems must besynchronized to include the volume's content before they can be used.Until synchronized, this leaves the copied volumes distinctly differentfrom the already synchronized volumes, in that fault handling differsand request handling from the not yet synced member storage systemseither won't work or must be forwarded or won't be as fast because readswill have to traverse an interconnect. Also, the internal implementationwill have to handle some volumes being in sync and ready for faulthandling and others not being in sync.

There are other problems relating to reliability of the operation in theface of faults. Coordinating a migration of volumes betweenmulti-storage-system pods is a distributed operation. If pods are theunit of fault handling and recovery, and if mediation or quorum orwhatever means are used to avoid split-brain situations, then a switchin volumes from one pod with a particular set of state andconfigurations and relationships for fault handling, recovery, mediationand quorum to another then storage systems in a pod have to be carefulabout coordinating changes related to that handling for any volumes.Operations can't be atomically distributed between storage systems, butmust be staged in some way. Mediation and quorum models essentiallyprovide pods with the tools for implementing distributed transactionalatomicity, but this may not extend to inter-pod operations withoutadding to the implementation.

Consider even a simple migration of a volume from a first pod to asecond pod even for two pods that share the same first and secondstorage systems. At some point the storage systems will coordinate todefine that the volume is now in the second pod and is no longer in thefirst pod. If there is no inherent mechanism for transactional atomicityacross the storage systems for the two pods, then a naive implementationcould leave the volume in the first pod on the first storage system andthe second pod on the second storage system at the time of a networkfault that results in fault handling to detach storage systems from thetwo pods. If pods separately determine which storage system succeeds indetaching the other, then the result could be that the same storagesystem detaches the other storage system for both pods, in which casethe result of the volume migration recovery should be consistent, or itcould result in a different storage system detaching the other for thetwo pods. If the first storage system detaches the second storage systemfor the first pod and the second storage system detaches the firststorage system for the second pod, then recovery might result in thevolume being recovered to the first pod on the first storage system andinto the second pod on the second storage system, with the volume thenrunning and exported to hosts and storage applications on both storagesystems. If instead the second storage system detaches the first storagesystem for the first pod and first storage detaches the second storagesystem for the second pod, then recovery might result in the volumebeing discarded from the second pod by the first storage system and thevolume being discarded from the first pod by the second storage system,resulting in the volume disappearing entirely. If the pods a volume isbeing migrated between are on differing sets of storage systems, thenthings can get even more complicated.

A solution to these problems may be to use an intermediate pod alongwith the techniques described previously for splitting and joining pods.This intermediate pod may never be presented as visible managed objectsassociated with the storage systems. In this model, volumes to be movedfrom a first pod to a second pod are first split from the first pod intoa new intermediate pod using the split operation described previously.The storage system members for the intermediate pod can then be adjustedto match the membership of storage systems by adding or removing storagesystems from the pod as necessary. Subsequently, the intermediate podcan be joined with the second pod.

For further explanation, FIG. 3E sets forth a flow chart illustrating anexample method for servicing I/O operations directed to a dataset(311-42) that is synchronized across a plurality of storage systems(311-38, 311-40) according to some embodiments of the presentdisclosure. Although depicted in less detail, the storage systems(311-38, 311-40) depicted in FIG. 3E may be similar to the storagesystems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIG.3A-3B, or any combination thereof. In fact, the storage system depictedin FIG. 3E may include the same, fewer, additional components as thestorage systems described above.

The dataset (311-42) depicted in FIG. 3E may be embodied, for example,as the contents of a particular volume, as the contents of a particularshared of a volume, or as any other collection of one or more dataelements. The dataset (311-42) may be synchronized across a plurality ofstorage systems (311-38, 311-40) such that each storage system (311-38,311-40) retains a local copy of the dataset (311-42). In the examplesdescribed herein, such a dataset (311-42) is synchronously replicatedacross the storage systems (311-38, 311-40) in such a way that thedataset (311-42) can be accessed through any of the storage systems(311-38, 311-40) with performance characteristics such that any onestorage system in the cluster doesn't operate substantially moreoptimally any other storage system in the cluster, at least as long asthe cluster and the particular storage system being accessed are runningnominally. In such systems, modifications to the dataset (311-42) shouldbe made to the copy of the dataset that resides on each storage system(311-38, 311-40) in such a way that accessing the dataset (311-42) onany storage system (311-38, 311-40) will yield consistent results. Forexample, a write request issued to the dataset must be serviced on allstorage systems (311-38, 311-40) or on none of the storage systems(311-38, 311-40) that were running nominally at the beginning of thewrite and that remained running nominally through completion of thewrite. Likewise, some groups of operations (e.g., two write operationsthat are directed to same location within the dataset) must be executedin the same order, or other steps must be taken as described in greaterdetail below, on all storage systems (311-38, 311-40) such that thedataset is ultimately identical on all storage systems (311-38, 311-40).Modifications to the dataset (311-42) need not be made at the exact sametime, but some actions (e.g., issuing an acknowledgement that a writerequest directed to the dataset, enabling read access to a locationwithin the dataset that is targeted by a write request that has not yetbeen completed on both storage systems) may be delayed until the copy ofthe dataset on each storage system (311-38, 311-40) has been modified.

In the example method depicted in FIG. 3E, the designation of onestorage system (311-40) as the ‘leader’ and another storage system(311-38) as the ‘follower’ may refer to the respective relationships ofeach storage system for the purposes of synchronously replicating aparticular dataset across the storage systems. In such an example, andas will be described in greater detail below, the leader storage system(311-40) may be responsible for performing some processing of anincoming I/O operation and passing such information along to thefollower storage system (311-38) or performing other tasks that are notrequired of the follower storage system (311-40). The leader storagesystem (311-40) may be responsible for performing tasks that are notrequired of the follower storage system (311-38) for all incoming I/Ooperations or, alternatively, the leader-follower relationship may bespecific to only a subset of the I/O operations that are received byeither storage system. For example, the leader-follower relationship maybe specific to I/O operations that are directed towards a first volume,a first group of volumes, a first group of logical addresses, a firstgroup of physical addresses, or some other logical or physicaldelineator. In such a way, a first storage system may serve as theleader storage system for I/O operations directed to a first set ofvolumes (or other delineator) while a second storage system may serve asthe leader storage system for I/O operations directed to a second set ofvolumes (or other delineator). The example method depicted in FIG. 3Edepicts an embodiment where synchronizing a plurality of storage systems(311-38, 311-40) occurs in response to the receipt of a request (311-04)to modify a dataset (311-42) by the leader storage system (311-40),although synchronizing a plurality of storage systems (311-38, 311-40)may also be carried out in response to the receipt of a request (311-04)to modify a dataset (311-42) by the follower storage system (311-38), aswill be described in greater detail below.

The example method depicted in FIG. 3E includes receiving (311-06), by aleader storage system (311-40), a request (311-04) to modify the dataset(311-42). The request (311-04) to modify the dataset (311-42) may beembodied, for example, as a request to write data to a location withinthe storage system (311-40) that contains data that is included in thedataset (311-42), as a request to write data to a volume that containsdata that is included in the dataset (311-42), as a request to take asnapshot of the dataset (311-42), as a virtual range copy, as an UNMAPoperation that essentially represents a deletion of some portion of thedata in the dataset (311-42), as a modifying transformations of thedataset (311-42) (rather than a change to a portion of data within thedataset), or as some other operation that results in a change to someportion of the data that is included in the dataset (311-42). In theexample method depicted in FIG. 3E, the request (311-04) to modify thedataset (311-42) is issued by a host (311-02) that may be embodied, forexample, as an application that is executing on a virtual machine, as anapplication that is executing on a computing device that is connected tothe storage system (311-40), or as some other entity configured toaccess the storage system (311-40).

The example method depicted in FIG. 3E also includes generating(311-08), by the leader storage system (311-40), information (311-10)describing the modification to the dataset (311-42). The leader storagesystem (311-40) may generate (311-08) the information (311-10)describing the modification to the dataset (311-42), for example, bydetermining ordering versus any other operations that are in progress,by determining the proper outcome of overlapping modifications (e.g.,the appropriate outcome of two requests to modify the same storagelocation), calculating any distributed state changes such as to commonelements of metadata across all members of the pod (e.g., all storagesystems across which the dataset is synchronously replicated), and soon. The information (311-10) describing the modification to the dataset(311-42) may be embodied, for example, as system-level information thatis used to describe an I/O operation that is to be performed by astorage system. The leader storage system (311-40) may generate (311-08)the information (311-10) describing the modification to the dataset(311-42) by processing the request (311-04) to modify the dataset(311-42) just enough to figure out what should happen in order toservice the request (311-04) to modify the dataset (311-42). Forexample, the leader storage system (311-40) may determine whether someordering of the execution of the request (311-04) to modify the dataset(311-42) relative to other requests to modify the dataset (311-42) isrequired, or some other steps must be taken as described in greaterdetail below, to produce an equivalent result on each storage system(311-38, 311-40).

Consider an example in which the request (311-04) to modify the dataset(311-42) is embodied as a request to copy blocks from a first addressrange in the dataset (311-42) to a second address range in the dataset(311-42). In such an example, assume that three other write operations(write A, write B, write C) are directed to the first address range inthe dataset (311-42). In such an example, if the leader storage system(311-40) services write A and write B (but does not service write C)prior to copying the blocks from the first address range in the dataset(311-42) to the second address range in the dataset (311-42), thefollower storage system (311-38) must also service write A and write B(but does not service write C) prior to copying the blocks from thefirst address range in the dataset (311-42) to the second address rangein the dataset (311-42) in order to yield consistent results. As such,when the leader storage system (311-40) generates (311-08) theinformation (311-10) describing the modification to the dataset(311-42), in this example, the leader storage system (311-40) couldgenerate information (e.g., sequence numbers for write A and write B)that identifies other operations that must be completed before thefollower storage system (311-38) can process the request (311-04) tomodify the dataset (311-42).

Consider an additional example in which two requests (e.g., Write A andWrite B) are directed to overlapping portions of the dataset (311-42).In such an example, if the leader storage system (311-40) services writeA and subsequently services write B, while the follower storage system(311-38) services write B and subsequently services write A, the dataset(311-42) would not be consistent across both storage systems (311-38,311-40). As such, when the leader storage system (311-40) generates(311-08) the information (311-10) describing the modification to thedataset (311-42), in this example, the leader storage system (311-40)could generate information (e.g., sequence numbers for write A and writeB) that identifies the order in which the requests should be executed.Alternatively, rather than generating information (311-10) describingthe modification to the dataset (311-42) which requires intermediatebehavior from each storage system (311-38, 311-40), the leader storagesystem (311-40) may generate (311-08) information (311-10) describingthe modification to the dataset (311-42) that includes information thatidentifies the proper outcome of the two requests. For example, if writeB logically follows write A (and overlaps with write A), the end resultmust be that the dataset (311-42) includes the parts of write B thatoverlap with write A, rather than including the parts of write A thatoverlap with write B. Such an outcome could be facilitated by merging aresult in memory and writing the result of such a merge to the dataset(311-42), rather than strictly requiring that a particular storagesystem (311-38, 311-40) execute write A and then subsequently executewrite B. Readers will appreciate that more subtle cases relate tosnapshots and virtual address range copies.

Readers will further appreciate that correct results for any operationmust be committed to the point of being recoverable before the operationcan be acknowledged. But, multiple operations can be committed together,or operations can be partially committed if recovery would ensurecorrectness. For example, a snapshot could locally commit with arecorded dependency on an expected write of A and B, but A or B mightnot have themselves committed. The snapshot cannot be acknowledged, andrecovery might end up backing out the snapshot if the missing I/O cannotbe recovered from another array. Also, if write B overlaps with write A,then the leader may “order” B to be after A, but A could actually bediscarded and the operation to write A would then simply wait for B.Writes A, B, C, and D, coupled with a snapshot between A,B and C,D couldcommit and/or acknowledge some or all parts together as long as recoverycannot result in a snapshot inconsistency across arrays and as long asacknowledgement does not complete a later operation before an earlieroperation has been persisted to the point that it is guaranteed to berecoverable.

The example method depicted in FIG. 3E also includes sending (311-12),from the leader storage system (311-40) to a follower storage system(311-38), information (311-10) describing the modification to thedataset (311-42). Sending (311-12) information (311-10) describing themodification to the dataset (311-42) from the leader storage system(311-40) to a follower storage system (311-38) may be carried out, forexample, by the leader storage system (311-40) sending one or moremessages to the follower storage system (311-38). The leader storagesystem (311-40) may also send, in the same messages or in one or moredifferent messages, I/O payload (311-14) for the request (311-04) tomodify the dataset (311-42). The I/O payload (311-14) may be embodied,for example, as data that is to be written to storage within thefollower storage system (311-38) when the request (311-04) to modify thedataset (311-42) is embodied as a request to write data to the dataset(311-42). In such an example, because the request (311-04) to modify thedataset (311-42) was received (311-06) by the leader storage system(311-40), the follower storage system (311-38) has not received the I/Opayload (311-14) associated with the request (311-04) to modify thedataset (311-42). In the example method depicted in FIG. 3E, theinformation (311-10) describing the modification to the dataset (311-42)and the I/O payload (311-14) that is associated with the request(311-04) to modify the dataset (311-42) may be sent (311-12) from theleader storage system (311-40) to the follower storage system (311-38)via one or more data communications networks that couple the leaderstorage system (311-40) to the follower storage system (311-38), via oneor more dedicated data communications links (e.g., a first link forsending I/O payload and a second link for sending information describingmodifications to datasets) that couples the leader storage system(311-40) to the follower storage system (311-38), or via some othermechanism.

The example method depicted in FIG. 3E also includes receiving (311-16),by the follower storage system (311-38), the information (311-10)describing the modification to the dataset (311-42). The followerstorage system (311-38) may receive (311-16) the information (311-10)describing the modification to the dataset (311-42) and I/O payload(311-14) from the leader storage system (311-40), for example, via oneor more messages that are sent from the leader storage system (311-40)to the follower storage system (311-38). The one or more messages may besent from the leader storage system (311-40) to the follower storagesystem (311-38) via one or more dedicated data communications linksbetween the two storage systems (311-38, 311-40), by the leader storagesystem (311-40) writing the message to a predetermined memory location(e.g., the location of a queue) on the follower storage system (311-38)using RDMA or a similar mechanism, or in other ways.

In one embodiment, the follower storage system (311-38) may receive(311-16) the information (311-10) describing the modification to thedataset (311-42) and I/O payload (311-14) from the leader storage system(311-40) through the use of the use of SCSI requests (writes from senderto receiver, or reads from receiver to sender) as a communicationmechanism. In such an embodiment, a SCSI Write request is used to encodeinformation that is intended to be sent (which includes whatever dataand metadata), and which may be delivered to a special pseudo-device orover a specially configured SCSI network, or through any other agreedupon addressing mechanism. Or, alternately, the model can issue a set ofopen SCSI read requests from a receiver to a sender, also using specialdevices, specially configured SCSI networks, or other agreed uponmechanisms. Encoded information including data and metadata will bedelivered to the receiver as a response to one or more of these openSCSI requests. Such a model can be implemented over Fibre Channel SCSInetworks, which are often deployed as the “dark fibre” storage networkinfrastructure between data centers. Such a model also allows the use ofthe same network lines for host-to-remote-array multipathing and bulkarray-to-array communications.

The example method depicted in FIG. 3E also includes processing(311-18), by the follower storage system (311-38), the request (311-04)to modify the dataset (311-42). In the example method depicted in FIG.3E, the follower storage system (311-38) may process (311-18) therequest (311-04) to modify the dataset (311-42) by modifying thecontents of one or more storage devices (e.g., an NVRAM device, an SSD,an HDD) that are included in the follower storage system (311-38) independence upon the information (311-10) describing the modification tothe dataset (311-42) as well as the I/O payload (311-14) that wasreceived from the leader storage system (311-40). Consider an example inwhich the request (311-04) to modify the dataset (311-42) is embodied asa write operation that is directed to a volume that is included in thedataset (311-42) and the information (311-10) describing themodification to the dataset (311-42) indicates that the write operationcan only be executed after a previously issued write operation has beenprocessed. In such an example, processing (311-18) the request (311-04)to modify the dataset (311-42) may be carried out by the followerstorage system (311-38) first verifying that the previously issued writeoperation has been processed on the follower storage system (311-38) andsubsequently writing I/O payload (311-14) associated with the writeoperation to one or more storage devices that are included in thefollower storage system (311-38). In such an example, the request(311-04) to modify the dataset (311-42) may be considered to have beencompleted and successfully processed, for example, when the I/O payload(311-14) has been committed to persistent storage within the followerstorage system (311-38).

The example method depicted in FIG. 3E also includes acknowledging(311-20), by the follower storage system (311-38) to the leader storagesystem (311-40), completion of the request (311-04) to modify thedataset (311-42). In the example method depicted in FIG. 3E,acknowledging (311-20), by the follower storage system (311-38) to theleader storage system (311-40), completion of the request (311-04) tomodify the dataset (311-42) may be carried out by the follower storagesystem (311-38) sending an acknowledgment (311-22) message to the leaderstorage system (311-40). Such messages may include, for example,information identifying the particular request (311-04) to modify thedataset (311-42) that was completed as well as any additionalinformation useful in acknowledging (311-20) the completion of therequest (311-04) to modify the dataset (311-42) by the follower storagesystem (311-38). In the example method depicted in FIG. 3E,acknowledging (311-20) completion of the request (311-04) to modify thedataset (311-42) to the leader storage system (311-40) is illustrated bythe follower storage system (311-38) issuing an acknowledgment (311-22)message to the leader storage system (311-38).

The example method depicted in FIG. 3E also includes processing(311-24), by the leader storage system (311-40), the request (311-04) tomodify the dataset (311-42). In the example method depicted in FIG. 3E,the leader storage system (311-40) may process (311-24) the request(311-04) to modify the dataset (311-42) by modifying the contents of oneor more storage devices (e.g., an NVRAM device, an SSD, an HDD) that areincluded in the leader storage system (311-40) in dependence upon theinformation (311-10) describing the modification to the dataset (311-42)as well as the I/O payload (311-14) that was received as part of therequest (311-04) to modify the dataset (311-42). Consider an example inwhich the request (311-04) to modify the dataset (311-42) is embodied asa write operation that is directed to a volume that is included in thedataset (311-42) and the information (311-10) describing themodification to the dataset (311-42) indicates that the write operationcan only be executed after a previously issued write operation has beenprocessed. In such an example, processing (311-24) the request (311-04)to modify the dataset (311-42) may be carried out by the leader storagesystem (311-40) first verifying that the previously issued writeoperation has been processed by the leader storage system (311-40) andsubsequently writing I/O payload (311-14) associated with the writeoperation to one or more storage devices that are included in the leaderstorage system (311-40). In such an example, the request (311-04) tomodify the dataset (311-42) may be considered to have been completed andsuccessfully processed, for example, when the I/O payload (311-14) hasbeen committed to persistent storage within the leader storage system(311-40).

The example method depicted in FIG. 3E also includes receiving (311-26),from the follower storage system (311-38), an indication that thefollower storage system (311-38) has processed the request (311-04) tomodify the dataset (311-42). In this example, the indication that thefollower storage system (311-38) has processed the request (311-04) tomodify the dataset (311-42) is embodied as an acknowledgement (311-22)message sent from the follower storage system (311-38) to the leaderstorage system (311-40). Readers will appreciate that although many ofthe steps described above are depicted and described as occurring in aparticular order, no particular order is actually required. In fact,because the follower storage system (311-38) and the leader storagesystem (311-40) are independent storage systems, each storage system maybe performing some of the steps described above in parallel. Forexample, the follower storage system (311-38) may receive (311-16) theinformation (311-10) describing the modification to the dataset(311-42), process (311-18) the request (311-04) to modify the dataset(311-42), or acknowledge (311-20) completion of the request (311-04) tomodify the dataset (311-42) before the leader storage system (311-40)has processed (311-24) the request (311-04) to modify the dataset(311-42). Alternatively, the leader storage system (311-40) may haveprocessed (311-24) the request (311-04) to modify the dataset (311-42)before the follower storage system (311-38) has received (311-16) theinformation (311-10) describing the modification to the dataset(311-42), processed (311-18) the request (311-04) to modify the dataset(311-42), or acknowledged (311-20) completion of the request (311-04) tomodify the dataset (311-42).

The example method depicted in FIG. 3E also includes acknowledging(311-34), by the leader storage system (311-40), completion of therequest (311-04) to modify the dataset (311-42). In the example methoddepicted in FIG. 3E, acknowledging (311-34) completion of the request(311-04) to modify the dataset (311-42) may be carried out through theuse of one or more acknowledgement (311-36) messages that are sent fromthe leader storage system (311-40) to the host (311-02) or via someother appropriate mechanism. In the example method depicted in FIG. 3E,the leader storage system (311-40) may determine (311-28) whether therequest (311-04) to modify the dataset (311-42) has been processed(311-18) by the follower storage system (311-38) prior to acknowledging(311-34) completion of the request (311-04) to modify the dataset(311-42). The leader storage system (311-40) may determine (311-28)whether the request (311-04) to modify the dataset (311-42) has beenprocessed (311-18) by the follower storage system (311-38), for example,by determining whether the leader storage system (311-40) has receivedan acknowledgment message or other message from the follower storagesystem (311-38) indicating that the request (311-04) to modify thedataset (311-42) has been processed (311-18) by the follower storagesystem (311-38). In such an example, if the leader storage system(311-40) affirmatively (311-30) determines that the request (311-04) tomodify the dataset (311-42) has been processed (311-18) by the followerstorage system (311-38) and also processed (311-24) by the leaderstorage system (311-38), the leader storage system (311-40) may proceedby acknowledging (311-34) completion of the request (311-04) to modifythe dataset (311-42) to the host (311-02) that initiated the request(311-04) to modify the dataset (311-42). If the leader storage system(311-40) determines that the request (311-04) to modify the dataset(311-42) has not (311-32) been processed (311-18) by the followerstorage system (311-38) or has not been processed (311-24) by the leaderstorage system (311-38), however, the leader storage system (311-40) maynot yet acknowledge (311-34) completion of the request (311-04) tomodify the dataset (311-42) to the host (311-02) that initiated therequest (311-04) to modify the dataset (311-42), as the leader storagesystem (311-40) may only acknowledge (311-34) completion of the request(311-04) to modify the dataset (311-42) to the host (311-02) thatinitiated the request (311-04) to modify the dataset (311-42) when therequest (311-04) to modify the dataset (311-42) has been successfullyprocessed on all storage systems (311-38, 311-40) across which a dataset(311-42) is synchronously replicated.

Readers will appreciate that in the example method depicted in FIG. 3E,sending (311-12), from the leader storage system (311-40) to a followerstorage system (311-38), information (311-10) describing themodification to the dataset (311-42) and acknowledging (311-20), by thefollower storage system (311-38) to the leader storage system (311-40),completion of the request (311-04) to modify the dataset (311-42) can becarried out using single roundtrip messaging. Single roundtrip messagingmay be used, for example, through the use of Fibre Channel as a datainterconnect. Typically, SCSI protocols are used with Fibre Channel.Such interconnects are commonly provisioned between data centers becausesome older replication technologies may be built to essentiallyreplicate data as SCSI transactions over Fibre Channel networks. Also,historically Fibre Channel SCSI infrastructure had less overhead andlower latencies than networks based on Ethernet and TCP/IP. Further,when data centers are internally connected to block storage arrays usingFibre Channel, the Fibre Channel networks may be stretched to other datacenters so that hosts in one data center can switch to accessing storagearrays in a remote data center when local storage arrays fail.

SCSI could be used as a general communication mechanism, even though itis normally designed for use with block storage protocols for storingand retrieving data in block-oriented volumes (or for tape). Forexample, SCSI READ or SCSI WRITE could be used to deliver or retrievemessage data between storage controllers in paired storage systems. Atypical implementation of SCSI WRITE requires two message round trips: aSCSI initiator sends a SCSI CDB describing the SCSI WRITE operation, aSCSI target receives that CDB and the SCSI target sends a “Ready toReceive” message to the SCSI initiator. The SCSI initiator then sendsdata to the SCSI target and when SCSI WRITE is complete the SCSI targetresponds to the SCSI initiator with a Success completion. A SCSI READrequest, on the other hand, requires only one round trip: the SCSIinitiator sends a SCSI CDB describing the SCSI READ operation, a SCSItarget receives that CDB and responds with data and then a Successcompletion. As a result, over distance, a SCSI READ incurs half of thedistance-related latency as a SCSI WRITE. Because of this, it may befaster for a data communications receiver to use SCSI READ requests toreceive messages than for a sender of messages to use SCSI WRITErequests to send data. Using SCSI READ simply requires a message senderto operate as a SCSI target, and for a message receiver to operate as aSCSI initiator. A message receiver may send some number of SCSI CDB READrequests to any message sender, and the message sender would respond toone of the outstanding CDB READ requests when message data is available.Since SCSI subsystems may timeout if a READ request is outstanding fortoo long (e.g., 10 seconds), READ requests should be responded to withina few seconds even if there is no message data to be sent.

SCSI tape requests, as described in the SCSI Stream Commands standardfrom the T10 Technical Committee of the InterNational Committee onInformation Technology Standards, support variable response data, whichcan be more flexible for returning variable-sized message data. The SCSIstandard also supports an Immediate mode for SCSI WRITE requests, whichcould allow single-round-trip SCSI WRITE commands. Readers willappreciate that many of the embodiments described below also utilizesingle roundtrip messaging.

For further explanation, FIG. 4 sets forth an example of a cloud-basedstorage system (403) in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 4, the cloud-based storagesystem (403) is created entirely in a cloud computing environment (402)such as, for example, Amazon Web Services (‘AWS’), Microsoft Azure,Google Cloud Platform, IBM Cloud, Oracle Cloud, and others. Thecloud-based storage system (403) may be used to provide services similarto the services that may be provided by the storage systems describedabove. For example, the cloud-based storage system (403) may be used toprovide block storage services to users of the cloud-based storagesystem (403), the cloud-based storage system (403) may be used toprovide storage services to users of the cloud-based storage system(403) through the use of solid-state storage, and so on.

The cloud-based storage system (403) depicted in FIG. 4 includes twocloud computing instances (404, 406) that each are used to support theexecution of a storage controller application (408, 410). The cloudcomputing instances (404, 406) may be embodied, for example, asinstances of cloud computing resources (e.g., virtual machines) that maybe provided by the cloud computing environment (402) to support theexecution of software applications such as the storage controllerapplication (408, 410). In one embodiment, the cloud computing instances(404, 406) may be embodied as Amazon Elastic Compute Cloud (‘EC2’)instances. In such an example, an Amazon Machine Image (‘AMI’) thatincludes the storage controller application (408, 410) may be booted tocreate and configure a virtual machine that may execute the storagecontroller application (408, 410).

In the example method depicted in FIG. 4, the storage controllerapplication (408, 410) may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application (408, 410) may be embodiedas a module of computer program instructions that, when executed,carries out the same tasks as the controllers (110A, 110B in FIG. 1A)described above such as writing data received from the users of thecloud-based storage system (403) to the cloud-based storage system(403), erasing data from the cloud-based storage system (403),retrieving data from the cloud-based storage system (403) and providingsuch data to users of the cloud-based storage system (403), monitoringand reporting of disk utilization and performance, performing redundancyoperations, such as Redundant Array of Independent Drives (‘RAID’) orRAID-like data redundancy operations, compressing data, encrypting data,deduplicating data, and so forth. Readers will appreciate that becausethere are two cloud computing instances (404, 406) that each include thestorage controller application (408, 410), in some embodiments one cloudcomputing instance (404) may operate as the primary controller asdescribed above while the other cloud computing instance (406) mayoperate as the secondary controller as described above. In such anexample, in order to save costs, the cloud computing instance (404) thatoperates as the primary controller may be deployed on a relativelyhigh-performance and relatively expensive cloud computing instance whilethe cloud computing instance (406) that operates as the secondarycontroller may be deployed on a relatively low-performance andrelatively inexpensive cloud computing instance. Readers will appreciatethat the storage controller application (408, 410) depicted in FIG. 4may include identical source code that is executed within differentcloud computing instances (404, 406).

Consider an example in which the cloud computing environment (402) isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, AWS offers many types of EC2 instances.For example, AWS offers a suite of general purpose EC2 instances thatinclude varying levels of memory and processing power. In such anexample, the cloud computing instance (404) that operates as the primarycontroller may be deployed on one of the instance types that has arelatively large amount of memory and processing power while the cloudcomputing instance (406) that operates as the secondary controller maybe deployed on one of the instance types that has a relatively smallamount of memory and processing power. In such an example, upon theoccurrence of a failover event where the roles of primary and secondaryare switched, a double failover may actually be carried out suchthat: 1) a first failover event where the cloud computing instance (406)that formerly operated as the secondary controller begins to operate asthe primary controller, and 2) a third cloud computing instance (notshown) that is of an instance type that has a relatively large amount ofmemory and processing power is spun up with a copy of the storagecontroller application, where the third cloud computing instance beginsoperating as the primary controller while the cloud computing instance(406) that originally operated as the secondary controller beginsoperating as the secondary controller again. In such an example, thecloud computing instance (404) that formerly operated as the primarycontroller may be terminated. Readers will appreciate that inalternative embodiments, the cloud computing instance (404) that isoperating as the secondary controller after the failover event maycontinue to operate as the secondary controller and the cloud computinginstance (406) that operated as the primary controller after theoccurrence of the failover event may be terminated once the primary rolehas been assumed by the third cloud computing instance (not shown).

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance (404) operatesas the primary controller and the second cloud computing instance (406)operates as the secondary controller, other embodiments are within thescope of the present disclosure. For example, each cloud computinginstance (404, 406) may operate as a primary controller for some portionof the address space supported by the cloud-based storage system (403),each cloud computing instance (404, 406) may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system (403) are divided in some other way, and soon. In fact, in other embodiments where costs savings may be prioritizedover performance demands, only a single cloud computing instance mayexist that contains the storage controller application. In such anexample, a controller failure may take more time to recover from as anew cloud computing instance that includes the storage controllerapplication would need to be spun up rather than having an alreadycreated cloud computing instance take on the role of servicing I/Ooperations that would have otherwise been handled by the failed cloudcomputing instance.

The cloud-based storage system (403) depicted in FIG. 4 includes cloudcomputing instances (424 a, 424 b, 424 n) with local storage (414, 418,422). The cloud computing instances (424 a, 424 b, 424 n) depicted inFIG. 4 may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment (402)to support the execution of software applications. The cloud computinginstances (424 a, 424 b, 424 n) of FIG. 4 may differ from the cloudcomputing instances (404, 406) described above as the cloud computinginstances (424 a, 424 b, 424 n) of FIG. 4 have local storage (414, 418,422) resources whereas the cloud computing instances (404, 406) thatsupport the execution of the storage controller application (408, 410)need not have local storage resources. The cloud computing instances(424 a, 424 b, 424 n) with local storage (414, 418, 422) may beembodied, for example, as EC2 M5 instances that include one or moreSSDs, as EC2 R5 instances that include one or more SSDs, as EC2 I3instances that include one or more SSDs, and so on. In some embodiments,the local storage (414, 418, 422) must be embodied as solid-statestorage (e.g., SSDs) rather than storage that makes use of hard diskdrives.

In the example depicted in FIG. 4, each of the cloud computing instances(424 a, 424 b, 424 n) with local storage (414, 418, 422) can include asoftware daemon (412, 416, 420) that, when executed by a cloud computinginstance (424 a, 424 b, 424 n) can present itself to the storagecontroller applications (408, 410) as if the cloud computing instance(424 a, 424 b, 424 n) were a physical storage device (e.g., one or moreSSDs). In such an example, the software daemon (412, 416, 420) mayinclude computer program instructions similar to those that wouldnormally be contained on a storage device such that the storagecontroller applications (408, 410) can send and receive the samecommands that a storage controller would send to storage devices. Insuch a way, the storage controller applications (408, 410) may includecode that is identical to (or substantially identical to) the code thatwould be executed by the controllers in the storage systems describedabove. In these and similar embodiments, communications between thestorage controller applications (408, 410) and the cloud computinginstances (424 a, 424 b, 424 n) with local storage (414, 418, 422) mayutilize iSCSI, NVMe over TCP, messaging, a custom protocol, or in someother mechanism.

In the example depicted in FIG. 4, each of the cloud computing instances(424 a, 424 b, 424 n) with local storage (414, 418, 422) may also becoupled to block-storage (426, 428, 430) that is offered by the cloudcomputing environment (402). The block-storage (426, 428, 430) that isoffered by the cloud computing environment (402) may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume (426) may be coupled to a first cloud computinginstance (424 a), a second EBS volume (428) may be coupled to a secondcloud computing instance (424 b), and a third EBS volume (430) may becoupled to a third cloud computing instance (424 n). In such an example,the block-storage (426, 428, 430) that is offered by the cloud computingenvironment (402) may be utilized in a manner that is similar to how theNVRAM devices described above are utilized, as the software daemon (412,416, 420) (or some other module) that is executing within a particularcloud comping instance (424 a, 424 b, 424 n) may, upon receiving arequest to write data, initiate a write of the data to its attached EBSvolume as well as a write of the data to its local storage (414, 418,422) resources. In some alternative embodiments, data may only bewritten to the local storage (414, 418, 422) resources within aparticular cloud comping instance (424 a, 424 b, 424 n). In analternative embodiment, rather than using the block-storage (426, 428,430) that is offered by the cloud computing environment (402) as NVRAM,actual RAM on each of the cloud computing instances (424 a, 424 b, 424n) with local storage (414, 418, 422) may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 4, the cloud computing instances (424 a,424 b, 424 n) with local storage (414, 418, 422) may be utilized, bycloud computing instances (404, 406) that support the execution of thestorage controller application (408, 410) to service I/O operations thatare directed to the cloud-based storage system (403). Consider anexample in which a first cloud computing instance (404) that isexecuting the storage controller application (408) is operating as theprimary controller. In such an example, the first cloud computinginstance (404) that is executing the storage controller application(408) may receive (directly or indirectly via the secondary controller)requests to write data to the cloud-based storage system (403) fromusers of the cloud-based storage system (403). In such an example, thefirst cloud computing instance (404) that is executing the storagecontroller application (408) may perform various tasks such as, forexample, deduplicating the data contained in the request, compressingthe data contained in the request, determining where to the write thedata contained in the request, and so on, before ultimately sending arequest to write a deduplicated, encrypted, or otherwise possiblyupdated version of the data to one or more of the cloud computinginstances (424 a, 424 b, 424 n) with local storage (414, 418, 422).Either cloud computing instance (404, 406), in some embodiments, mayreceive a request to read data from the cloud-based storage system (403)and may ultimately send a request to read data to one or more of thecloud computing instances (424 a, 424 b, 424 n) with local storage (414,418, 422).

Readers will appreciate that when a request to write data is received bya particular cloud computing instance (424 a, 424 b, 424 n) with localstorage (414, 418, 422), the software daemon (412, 416, 420) or someother module of computer program instructions that is executing on theparticular cloud computing instance (424 a, 424 b, 424 n) may beconfigured to not only write the data to its own local storage (414,418, 422) resources and any appropriate block storage (426, 428, 430)that are offered by the cloud computing environment (402), but thesoftware daemon (412, 416, 420) or some other module of computer programinstructions that is executing on the particular cloud computinginstance (424 a, 424 b, 424 n) may also be configured to write the datato cloud-based object storage (432) that is attached to the particularcloud computing instance (424 a, 424 b, 424 n). The cloud-based objectstorage (432) that is attached to the particular cloud computinginstance (424 a, 424 b, 424 n) may be embodied, for example, as AmazonSimple Storage Service (‘S3’) storage that is accessible by theparticular cloud computing instance (424 a, 424 b, 424 n). In otherembodiments, the cloud computing instances (404, 406) that each includethe storage controller application (408, 410) may initiate the storageof the data in the local storage (414, 418, 422) of the cloud computinginstances (424 a, 424 b, 424 n) and the cloud-based object storage(432).

Readers will appreciate that the software daemon (412, 416, 420) orother module of computer program instructions that writes the data toblock storage (e.g., local storage (414, 418, 422) resources) and alsowrites the data to cloud-based object storage (432) may be executed onprocessing units of dissimilar types (e.g., different types cloudcomputing instances, cloud computing instances that contain differentprocessing units). In fact, the software daemon (412, 416, 420) or othermodule of computer program instructions that writes the data to blockstorage (e.g., local storage (414, 418, 422) resources) and also writesthe data to cloud-based object storage (432) can be migrated betweendifferent types cloud computing instances based on demand.

Readers will appreciate that, as described above, the cloud-basedstorage system (403) may be used to provide block storage services tousers of the cloud-based storage system (403). While the local storage(414, 418, 422) resources and the block-storage (426, 428, 430)resources that are utilized by the cloud computing instances (424 a, 424b, 424 n) may support block-level access, the cloud-based object storage(432) that is attached to the particular cloud computing instance (424a, 424 b, 424 n) supports only object-based access. In order to addressthis, the software daemon (412, 416, 420) or some other module ofcomputer program instructions that is executing on the particular cloudcomputing instance (424 a, 424 b, 424 n) may be configured to takeblocks of data, package those blocks into objects, and write the objectsto the cloud-based object storage (432) that is attached to theparticular cloud computing instance (424 a, 424 b, 424 n).

Consider an example in which data is written to the local storage (414,418, 422) resources and the block-storage (426, 428, 430) resources thatare utilized by the cloud computing instances (424 a, 424 b, 424 n) in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system (403) issues a request to write data that, after beingcompressed and deduplicated by the storage controller application (408,410) results in the need to write 5 MB of data. In such an example,writing the data to the local storage (414, 418, 422) resources and theblock-storage (426, 428, 430) resources that are utilized by the cloudcomputing instances (424 a, 424 b, 424 n) is relatively straightforwardas 5 blocks that are 1 MB in size are written to the local storage (414,418, 422) resources and the block-storage (426, 428, 430) resources thatare utilized by the cloud computing instances (424 a, 424 b, 424 n). Insuch an example, the software daemon (412, 416, 420) or some othermodule of computer program instructions that is executing on theparticular cloud computing instance (424 a, 424 b, 424 n) may beconfigured to: 1) create a first object that includes the first 1 MB ofdata and write the first object to the cloud-based object storage (432),2) create a second object that includes the second 1 MB of data andwrite the second object to the cloud-based object storage (432), 3)create a third object that includes the third 1 MB of data and write thethird object to the cloud-based object storage (432), and so on. Assuch, in some embodiments, each object that is written to thecloud-based object storage (432) may be identical (or nearly identical)in size. Readers will appreciate that in such an example, metadata thatis associated with the data itself may be included in each object (e.g.,the first 1 MB of the object is data and the remaining portion ismetadata associated with the data).

Readers will appreciate that the cloud-based object storage (432) may beincorporated into the cloud-based storage system (403) to increase thedurability of the cloud-based storage system (403). Continuing with theexample described above where the cloud computing instances (424 a, 424b, 424 n) are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances (424 a, 424b, 424 n) with local storage (414, 418, 422) as the only source ofpersistent data storage in the cloud-based storage system (403) mayresult in a relatively unreliable storage system. Likewise, EBS volumesare designed for 99.999% availability. As such, even relying on EBS asthe persistent data store in the cloud-based storage system (403) mayresult in a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system (403) that can incorporate S3 into its poolof storage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system (403)that can incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system (403) depicted in FIG. 4 not only stores data in S3 butthe cloud-based storage system (403) also stores data in local storage(414, 418, 422) resources and block-storage (426, 428, 430) resourcesthat are utilized by the cloud computing instances (424 a, 424 b, 424n), such that read operations can be serviced from local storage (414,418, 422) resources and the block-storage (426, 428, 430) resources thatare utilized by the cloud computing instances (424 a, 424 b, 424 n),thereby reducing read latency when users of the cloud-based storagesystem (403) attempt to read data from the cloud-based storage system(403).

In some embodiments, all data that is stored by the cloud-based storagesystem (403) may be stored in both: 1) the cloud-based object storage(432), and 2) at least one of the local storage (414, 418, 422)resources or block-storage (426, 428, 430) resources that are utilizedby the cloud computing instances (424 a, 424 b, 424 n). In suchembodiments, the local storage (414, 418, 422) resources andblock-storage (426, 428, 430) resources that are utilized by the cloudcomputing instances (424 a, 424 b, 424 n) may effectively operate ascache that generally includes all data that is also stored in S3, suchthat all reads of data may be serviced by the cloud computing instances(424 a, 424 b, 424 n) without requiring the cloud computing instances(424 a, 424 b, 424 n) to access the cloud-based object storage (432).Readers will appreciate that in other embodiments, however, all datathat is stored by the cloud-based storage system (403) may be stored inthe cloud-based object storage (432), but less than all data that isstored by the cloud-based storage system (403) may be stored in at leastone of the local storage (414, 418, 422) resources or block-storage(426, 428, 430) resources that are utilized by the cloud computinginstances (424 a, 424 b, 424 n). In such an example, various policiesmay be utilized to determine which subset of the data that is stored bythe cloud-based storage system (403) should reside in both: 1) thecloud-based object storage (432), and 2) at least one of the localstorage (414, 418, 422) resources or block-storage (426, 428, 430)resources that are utilized by the cloud computing instances (424 a, 424b, 424 n).

As described above, when the cloud computing instances (424 a, 424 b,424 n) with local storage (414, 418, 422) are embodied as EC2 instances,the cloud computing instances (424 a, 424 b, 424 n) with local storage(414, 418, 422) are only guaranteed to have a monthly uptime of 99.9%and data stored in the local instance store only persists during thelifetime of each cloud computing instance (424 a, 424 b, 424 n) withlocal storage (414, 418, 422). As such, one or more modules of computerprogram instructions that are executing within the cloud-based storagesystem (403) (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances (424 a, 424 b, 424 n) with local storage (414,418, 422). In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances (424 a, 424 b,424 n) with local storage (414, 418, 422) by creating one or more newcloud computing instances with local storage, retrieving data that wasstored on the failed cloud computing instances (424 a, 424 b, 424 n)from the cloud-based object storage (432), and storing the dataretrieved from the cloud-based object storage (432) in local storage onthe newly created cloud computing instances. Readers will appreciatethat many variants of this process may be implemented.

Consider an example in which all cloud computing instances (424 a, 424b, 424 n) with local storage (414, 418, 422) failed. In such an example,the monitoring module may create new cloud computing instances withlocal storage, where high-bandwidth instances types are selected thatallow for the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage (432). Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage (432) such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage (432) asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage(432), less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system (403). The numberof new cloud computing instances that are created may substantiallyexceed the number of cloud computing instances that are needed tolocally store all of the data stored by the cloud-based storage system(403) in order to more rapidly pull data from the cloud-based objectstorage (432) and into the new cloud computing instances, as each newcloud computing instance can (in parallel) retrieve some portion of thedata stored by the cloud-based storage system (403). In suchembodiments, once the data stored by the cloud-based storage system(403) has been pulled into the newly created cloud computing instances,the data may be consolidated within a subset of the newly created cloudcomputing instances and those newly created cloud computing instancesthat are excessive may be terminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system (403) have written to the cloud-based storage system(403). In such an example, assume that all 1,000 cloud computinginstances fail. In such an example, the monitoring module may cause100,000 cloud computing instances to be created, where each cloudcomputing instance is responsible for retrieving, from the cloud-basedobject storage (432), distinct 1/100,000^(th) chunks of the valid datathat users of the cloud-based storage system (403) have written to thecloud-based storage system (403) and locally storing the distinct chunkof the dataset that it retrieved. In such an example, because each ofthe 100,000 cloud computing instances can retrieve data from thecloud-based object storage (432) in parallel, the caching layer may berestored 100 times faster as compared to an embodiment where themonitoring module only create 1000 replacement cloud computinginstances. In such an example, over time the data that is stored locallyin the 100,000 could be consolidated into 1,000 cloud computinginstances and the remaining 99,000 cloud computing instances could beterminated.

Readers will appreciate that various performance aspects of thecloud-based storage system (403) may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system (403) can be scaled-up or scaled-out as needed. Consideran example in which the monitoring module monitors the performance ofthe cloud-based storage system (403) via communications with one or moreof the cloud computing instances (404, 406) that each are used tosupport the execution of a storage controller application (408, 410),via monitoring communications between cloud computing instances (404,406, 424 a, 424 b, 424 n), via monitoring communications between cloudcomputing instances (404, 406, 424 a, 424 b, 424 n) and the cloud-basedobject storage (432), or in some other way. In such an example, assumethat the monitoring module determines that the cloud computing instances(404, 406) that are used to support the execution of a storagecontroller application (408, 410) are undersized and not sufficientlyservicing the I/O requests that are issued by users of the cloud-basedstorage system (403). In such an example, the monitoring module maycreate a new, more powerful cloud computing instance (e.g., a cloudcomputing instance of a type that includes more processing power, morememory, etc. . . . ) that includes the storage controller applicationsuch that the new, more powerful cloud computing instance can beginoperating as the primary controller. Likewise, if the monitoring moduledetermines that the cloud computing instances (404, 406) that are usedto support the execution of a storage controller application (408, 410)are oversized and that cost savings could be gained by switching to asmaller, less powerful cloud computing instance, the monitoring modulemay create a new, less powerful (and less expensive) cloud computinginstance that includes the storage controller application such that thenew, less powerful cloud computing instance can begin operating as theprimary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system (403), an example in which the monitoring moduledetermines that the utilization of the local storage that iscollectively provided by the cloud computing instances (424 a, 424 b,424 n) has reached a predetermined utilization threshold (e.g., 95%). Insuch an example, the monitoring module may create additional cloudcomputing instances with local storage to expand the pool of localstorage that is offered by the cloud computing instances. Alternatively,the monitoring module may create one or more new cloud computinginstances that have larger amounts of local storage than the alreadyexisting cloud computing instances (424 a, 424 b, 424 n), such that datastored in an already existing cloud computing instance (424 a, 424 b,424 n) can be migrated to the one or more new cloud computing instancesand the already existing cloud computing instance (424 a, 424 b, 424 n)can be terminated, thereby expanding the pool of local storage that isoffered by the cloud computing instances. Likewise, if the pool of localstorage that is offered by the cloud computing instances isunnecessarily large, data can be consolidated and some cloud computinginstances can be terminated.

Readers will appreciate that the cloud-based storage system (403) may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system (403), butthe monitoring module may also apply predictive policies that are basedon, for example, observed behavior (e.g., every night from 10 PM until 6AM usage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system (403) may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem (403) may be dynamically scaled, the cloud-based storage system(403) may even operate in a way that is more dynamic. Consider theexample of garbage collection. In a traditional storage system, theamount of storage is fixed. As such, at some point the storage systemmay be forced to perform garbage collection as the amount of availablestorage has become so constrained that the storage system is on theverge of running out of storage. In contrast, the cloud-based storagesystem (403) described here can always ‘add’ additional storage (e.g.,by adding more cloud computing instances with local storage). Becausethe cloud-based storage system (403) described here can always ‘add’additional storage, the cloud-based storage system (403) can make moreintelligent decisions regarding when to perform garbage collection. Forexample, the cloud-based storage system (403) may implement a policythat garbage collection only be performed when the number of IOPS beingserviced by the cloud-based storage system (403) falls below a certainlevel. In some embodiments, other system-level functions (e.g.,deduplication, compression) may also be turned off and on in response tosystem load, given that the size of the cloud-based storage system (403)is not constrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 4.

In some embodiments, especially in embodiments where the cloud-basedobject storage (432) resources are embodied as Amazon S3, thecloud-based storage system (403) may include one or more modules (e.g.,a module of computer program instructions executing on an EC2 instance)that are configured to ensure that when the local storage of aparticular cloud computing instance is rehydrated with data from S3, theappropriate data is actually in S3. This issue arises largely because S3implements an eventual consistency model where, when overwriting anexisting object, reads of the object will eventually (but notnecessarily immediately) become consistent and will eventually (but notnecessarily immediately) return the overwritten version of the object.To address this issue, in some embodiments of the present disclosure,objects in S3 are never overwritten. Instead, a traditional ‘overwrite’would result in the creation of the new object (that includes theupdated version of the data) and the eventual deletion of the old object(that includes the previous version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system (403) does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

In the example depicted in FIG. 4, and as described above, the cloudcomputing instances (404, 406) that are used to support the execution ofthe storage controller applications (408, 410) may operate in aprimary/secondary configuration where one of the cloud computinginstances (404, 406) that are used to support the execution of thestorage controller applications (408, 410) is responsible for writingdata to the local storage (414, 418, 422) that is attached to the cloudcomputing instances with local storage (424 a, 424 b, 424 n). In such anexample, however, because each of the cloud computing instances (404,406) that are used to support the execution of the storage controllerapplications (408, 410) can access the cloud computing instances withlocal storage (424 a, 424 b, 424 n), both of the cloud computinginstances (404, 406) that are used to support the execution of thestorage controller applications (408, 410) can service requests to readdata from the cloud-based storage system (403).

For further explanation, FIG. 5 sets forth an example of an additionalcloud-based storage system (502) in accordance with some embodiments ofthe present disclosure. In the example depicted in FIG. 5, thecloud-based storage system (502) is created entirely in a cloudcomputing environment (402) such as, for example, AWS, Microsoft Azure,Google Cloud Platform, IBM Cloud, Oracle Cloud, and others. Thecloud-based storage system (502) may be used to provide services similarto the services that may be provided by the storage systems describedabove. For example, the cloud-based storage system (502) may be used toprovide block storage services to users of the cloud-based storagesystem (502), the cloud-based storage system (403) may be used toprovide storage services to users of the cloud-based storage system(403) through the use of solid-state storage, and so on.

The cloud-based storage system (502) depicted in FIG. 5 may operate in amanner that is somewhat similar to the cloud-based storage system (403)depicted in FIG. 4, as the cloud-based storage system (502) depicted inFIG. 5 includes a storage controller application (506) that is beingexecuted in a cloud computing instance (504). In the example depicted inFIG. 5, however, the cloud computing instance (504) that executes thestorage controller application (506) is a cloud computing instance (504)with local storage (508). In such an example, data written to thecloud-based storage system (502) may be stored in both the local storage(508) of the cloud computing instance (504) and also in cloud-basedobject storage (510) in the same manner that the cloud-based objectstorage (510) was used above. In some embodiments, for example, thestorage controller application (506) may be responsible for writing datato the local storage (508) of the cloud computing instance (504) while asoftware daemon (512) may be responsible for ensuring that the data iswritten to the cloud-based object storage (510) in the same manner thatthe cloud-based object storage (510) was used above. In otherembodiments, the same entity (e.g., the storage controller application)may be responsible for writing data to the local storage (508) of thecloud computing instance (504) and also responsible for ensuring thatthe data is written to the cloud-based object storage (510) in the samemanner that the cloud-based object storage (510) was used above.

Readers will appreciate that a cloud-based storage system (502) depictedin FIG. 5 may represent a less expensive, less robust version of acloud-based storage system than was depicted in FIG. 4. In yetalternative embodiments, the cloud-based storage system (502) depictedin FIG. 5 could include additional cloud computing instances with localstorage that supported the execution of the storage controllerapplication (506), such that failover can occur if the cloud computinginstance (504) that executes the storage controller application (506)fails. Likewise, in other embodiments, the cloud-based storage system(502) depicted in FIG. 5 can include additional cloud computinginstances with local storage to expand the amount local storage that isoffered by the cloud computing instances in the cloud-based storagesystem (502).

Readers will appreciate that many of the failure scenarios describedabove with reference to FIG. 4 would also apply cloud-based storagesystem (502) depicted in FIG. 5. Likewise, the cloud-based storagesystem (502) depicted in FIG. 5 may be dynamically scaled up and down ina similar manner as described above. The performance of varioussystem-level tasks may also be executed by the cloud-based storagesystem (502) depicted in FIG. 5 in an intelligent way, as describedabove.

Readers will appreciate that, in an effort to increase the resiliency ofthe cloud-based storage systems described above, various components maybe located within different availability zones. For example, a firstcloud computing instance that supports the execution of the storagecontroller application may be located within a first availability zonewhile a second cloud computing instance that also supports the executionof the storage controller application may be located within a secondavailability zone. Likewise, the cloud computing instances with localstorage may be distributed across multiple availability zones. In fact,in some embodiments, an entire second cloud-based storage system couldbe created in a different availability zone, where data in the originalcloud-based storage system is replicated (synchronously orasynchronously) to the second cloud-based storage system so that if theentire original cloud-based storage system went down, a replacementcloud-based storage system (the second cloud-based storage system) couldbe brought up in a trivial amount of time.

Readers will appreciate that the cloud-based storage systems describedherein may be used as part of a fleet of storage systems. In fact, thecloud-based storage systems described herein may be paired withon-premises storage systems. In such an example, data stored in theon-premises storage may be replicated (synchronously or asynchronously)to the cloud-based storage system, and vice versa.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method of servicing I/O operations in a cloud-based storagesystem (604). Although depicted in less detail, the cloud-based storagesystem (604) depicted in FIG. 6 may be similar to the cloud-basedstorage systems described above and may be supported by a cloudcomputing environment (602).

The example method depicted in FIG. 6 includes receiving (606), by thecloud-based storage system (604), a request to write data to thecloud-based storage system (604). The request to write data may bereceived, for example, from an application executing in the cloudcomputing environment, by a user of the storage system that iscommunicatively coupled to the cloud computing environment, and in otherways. In such an example, the request can include the data that is to bewritten to the cloud-based storage system (604). In other embodiments,the request to write data to the cloud-based storage system (604) mayoccur at boot-time when the cloud-based storage system (604) is beingbrought up.

The example method depicted in FIG. 6 also includes deduplicating (608)the data. Data deduplication is a data reduction technique foreliminating duplicate copies of repeating data. The cloud-based storagesystem (604) may deduplicate (608) the data, for example, by comparingone or more portions of the data to data that is already stored in thecloud-based storage system (604), by comparing a fingerprint for one ormore portions of the data to fingerprints for data that is alreadystored in the cloud-based storage system (604), or in other ways. Insuch an example, duplicate data may be removed and replaced by areference to an already existing copy of the data that is already storedin the cloud-based storage system (604).

The example method depicted in FIG. 6 also includes compressing (610)the data. Data compression is a data reduction technique wherebyinformation is encoded using fewer bits than the originalrepresentation. The cloud-based storage system (604) may compress (610)the data by applying one or more data compression algorithms to thedata, which at this point may not include data that data that is alreadystored in the cloud-based storage system (604).

The example method depicted in FIG. 6 also includes encrypting (612) thedata. Data encryption is a technique that involves the conversion ofdata from a readable format into an encoded format that can only be reador processed after the data has been decrypted. The cloud-based storagesystem (604) may encrypt (612) the data, which at this point may havealready been deduplicated and compressed, using an encryption key.Readers will appreciate that although the embodiment depicted in FIG. 6involves deduplicating (608) the data, compressing (610) the data, andencrypting (612) the data, other embodiments exist in which fewer ofthese steps are performed and embodiment exist in which the same numberof steps or fewer are performed in a different order.

The example method depicted in FIG. 6 also includes storing (614), inblock storage of the cloud-based storage system (604), the data. Storing(614) the data in block storage of the cloud-based storage system (604)may be carried out, for example, by storing (616) the data solid-statestorage such as local storage (e.g., SSDs) of one or more cloudcomputing instances, as described in more detail above. In such anexample, the data may be spread across the local storage of many cloudcomputing instances, along with parity data, to implement RAID orRAID-like data redundancy.

The example method depicted in FIG. 6 also includes storing (618), inobject storage of the cloud-based storage system (604), the data.Storing (618) the data in object storage of the cloud-based storagesystem can include creating (620) one or more equal sized objects, whereeach equal sized object includes a distinct chunk of the data. In suchan example, because each object includes data and metadata, the dataportion of each object may be equal sized. In other embodiments, thedata portion of each created object may not be equal sized. For example,each object could include the data from a predetermined number of blocksin the block storage that was used in the preceding paragraph, or insome other way.

The example method depicted in FIG. 6 also includes receiving (622), bythe cloud-based storage system, a request to read data from thecloud-based storage system (604). The request to read data from thecloud-based storage system (604) may be received, for example, from anapplication executing in the cloud computing environment, by a user ofthe storage system that is communicatively coupled to the cloudcomputing environment, and in other ways. The request can include, forexample, a logical address the data that is to be read from thecloud-based storage system (604).

The example method depicted in FIG. 6 also includes retrieving (624),from block storage of the cloud-based storage system (604), the data.Readers will appreciate that the cloud-based storage system (604) mayretrieve (624) the data from block storage of the cloud-based storagesystem (604), for example, by the storage controller applicationforwarding the read request to the cloud computing instance thatincludes the requested data in its local storage. Readers willappreciate that by retrieving (624) the data from block storage of thecloud-based storage system (604), the data may be retrieved more rapidlythan if the data were read from cloud-based object storage, even thoughthe cloud-based object storage does include a copy of the data.

Readers will appreciate that in the example method depicted in FIG. 6,the block storage of the cloud-based storage system (604) ischaracterized by a low read latency relative to the object storage ofthe cloud-based storage system. As such, by servicing read operationsfrom the block storage rather than the object storage, the cloud-basedstorage system (604) may be able to service read operations using lowlatency block storage, while still offering the resiliency that isassociated with object storage solutions offered by cloud servicesproviders. Furthermore, the block storage of the cloud-based storagesystem (604) may offer relatively high bandwidth. The block storage ofthe cloud-based storage system (604) may be implemented in a variety ofways as will occur to readers of this disclosure.

For further explanation, FIG. 7 sets forth a flow chart illustrating anadditional example method of servicing I/O operations in a cloud-basedstorage system (604). The example method depicted in FIG. 7 is similarto the example method depicted in FIG. 6, as the example method depictedin FIG. 7 also includes receiving (606) a request to write data to thecloud-based storage system (604), storing (614) the data in blockstorage of the cloud-based storage system (604), and storing (618) thedata in object storage of the cloud-based storage system (604).

The example method depicted in FIG. 7 also includes detecting (702) thatat least some portion of the block storage of the cloud-based storagesystem has become unavailable. Detecting (702) that at least someportion of the block storage of the cloud-based storage system hasbecome unavailable may be carried out, for example, by detecting thatone or more of the cloud computing instances that includes local storagehas become unavailable, as described in greater detail below.

The example method depicted in FIG. 7 also includes identifying (704)data that was stored in the portion of the block storage of thecloud-based storage system that has become unavailable. Identifying(704) data that was stored in the portion of the block storage of thecloud-based storage system that has become unavailable may be carriedout, for example, through the use of metadata that maps some identifierof a piece of data (e.g., a sequence number, an address) to the locationwhere the data is stored. Such metadata, or separate metadata, may alsomap the piece of data to one or more object identifiers that identifyobjects stored in the object storage of the cloud-based storage systemthat contain the piece of data.

The example method depicted in FIG. 7 also includes retrieving (706),from object storage of the cloud-based storage system, the data that wasstored in the portion of the block storage of the cloud-based storagesystem that has become unavailable. Retrieving (706) the data that wasstored in the portion of the block storage of the cloud-based storagesystem that has become unavailable from object storage of thecloud-based storage system may be carried out, for example, through theuse of metadata described above that maps the data that was stored inthe portion of the block storage of the cloud-based storage system thathas become unavailable to one or more objects stored in the objectstorage of the cloud-based storage system that contain the piece ofdata. In such an example, retrieving (706) the data may be carried outby reading the objects that map to the data from the object storage ofthe cloud-based storage system.

The example method depicted in FIG. 7 also includes storing (708), inblock storage of the cloud-based storage system, the retrieved data.Storing (708) the retrieved data in block storage of the cloud-basedstorage system may be carried out, for example, by creating replacementcloud computing instances with local storage and storing the data in thelocal storage of one or more of the replacement cloud computinginstances, as described in greater detail above.

Readers will appreciate that although the embodiments described aboverelate to embodiments in which data that was stored in the portion ofthe block storage of the cloud-based storage system that has becomeunavailable is essentially brought back into the block storage layer ofthe cloud-based storage system by retrieving the data from the objectstorage layer of the cloud-based storage system, other embodiments arewithin the scope of the present disclosure. For example, because datamay be distributed across the local storage of multiple cloud computinginstances using data redundancy techniques such as RAID, in someembodiments the lost data may be brought back into the block storagelayer of the cloud-based storage system through a RAID rebuild.

For further explanation, FIG. 8 sets forth a flow chart illustrating anexample method of servicing I/O operations in a cloud-based storagesystem (804). Although depicted in less detail, the cloud-based storagesystem (804) depicted in FIG. 8 may be similar to the cloud-basedstorage systems described above and may be supported by a cloudcomputing environment (802).

The example method depicted in FIG. 8 includes receiving (806), by thecloud-based storage system (804), a request to write data to thecloud-based storage system (804). The request to write data may bereceived, for example, from an application executing in the cloudcomputing environment, by a user of the storage system that iscommunicatively coupled to the cloud computing environment, and in otherways. In such an example, the request can include the data that is to bewritten to the cloud-based storage system (804). In other embodiments,the request to write data to the cloud-based storage system (804) mayoccur at boot-time when the cloud-based storage system (804) is beingbrought up.

The example method depicted in FIG. 8 also includes deduplicating (808)the data. Data deduplication is a data reduction technique foreliminating duplicate copies of repeating data. The cloud-based storagesystem (804) may deduplicate (808) the data, for example, by comparingone or more portions of the data to data that is already stored in thecloud-based storage system (804), by comparing a fingerprint for one ormore portions of the data to fingerprints for data that is alreadystored in the cloud-based storage system (804), or in other ways. Insuch an example, duplicate data may be removed and replaced by areference to an already existing copy of the data that is already storedin the cloud-based storage system (804).

The example method depicted in FIG. 8 also includes compressing (810)the data. Data compression is a data reduction technique wherebyinformation is encoded using fewer bits than the originalrepresentation. The cloud-based storage system (804) may compress (810)the data by applying one or more data compression algorithms to thedata, which at this point may not include data that data that is alreadystored in the cloud-based storage system (804).

The example method depicted in FIG. 8 also includes encrypting (812) thedata. Data encryption is a technique that involves the conversion ofdata from a readable format into an encoded format that can only be reador processed after the data has been decrypted. The cloud-based storagesystem (804) may encrypt (812) the data, which at this point may havealready been deduplicated and compressed, using an encryption key.Readers will appreciate that although the embodiment depicted in FIG. 8involves deduplicating (808) the data, compressing (810) the data, andencrypting (812) the data, other embodiments exist in which fewer ofthese steps are performed and embodiment exist in which the same numberof steps or fewer are performed in a different order.

The example method depicted in FIG. 8 also includes storing (814), inblock storage of the cloud-based storage system (804), the data. Storing(814) the data in block storage of the cloud-based storage system (804)may be carried out, for example, by storing (816) the data in localstorage (e.g., SSDs) of one or more cloud computing instances, asdescribed in more detail above. In such an example, the data spreadacross local storage of multiple cloud computing instances, along withparity data, to implement RAID or RAID-like data redundancy.

The example method depicted in FIG. 8 also includes storing (818), inobject storage of the cloud-based storage system (804), the data.Storing (818) the data in object storage of the cloud-based storagesystem can include creating (820) one or more equal sized objects,wherein each equal sized object includes a distinct chunk of the data,as described in greater detail above.

The example method depicted in FIG. 8 also includes receiving (822), bythe cloud-based storage system, a request to read data from thecloud-based storage system (804). The request to read data from thecloud-based storage system (804) may be received, for example, from anapplication executing in the cloud computing environment, by a user ofthe storage system that is communicatively coupled to the cloudcomputing environment, and in other ways. The request can include, forexample, a logical address the data that is to be read from thecloud-based storage system (804).

The example method depicted in FIG. 8 also includes retrieving (824),from block storage of the cloud-based storage system (804), the data.Readers will appreciate that the cloud-based storage system (804) mayretrieve (824) the data from block storage of the cloud-based storagesystem (804), for example, by the storage controller applicationforwarding the read request to the cloud computing instance thatincludes the requested data in its local storage. Readers willappreciate that by retrieving (824) the data from block storage of thecloud-based storage system (804), the data may be retrieved more rapidlythan if the data were read from cloud-based object storage, even thoughthe cloud-based object storage does include a copy of the data.

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method of servicing I/O operations in a cloud-basedstorage system (804). The example method depicted in FIG. 9 is similarto the example method depicted in FIG. 8, as the example method depictedin FIG. 9 also includes receiving (806) a request to write data to thecloud-based storage system (804), storing (814) the data in blockstorage of the cloud-based storage system (804), and storing (818) thedata in object storage of the cloud-based storage system (804).

The example method depicted in FIG. 9 also includes detecting (902) thatat least some portion of the block storage of the cloud-based storagesystem has become unavailable. Detecting (902) that at least someportion of the block storage of the cloud-based storage system hasbecome unavailable may be carried out, for example, by detecting thatone or more of the cloud computing instances that includes local storagehas become unavailable, as described in greater detail below.

The example method depicted in FIG. 9 also includes identifying (904)data that was stored in the portion of the block storage of thecloud-based storage system that has become unavailable. Identifying(904) data that was stored in the portion of the block storage of thecloud-based storage system that has become unavailable may be carriedout, for example, through the use of metadata that maps some identifierof a piece of data (e.g., a sequence number, an address) to the locationwhere the data is stored. Such metadata, or separate metadata, may alsomap the piece of data to one or more object identifiers that identifyobjects stored in the object storage of the cloud-based storage systemthat contain the piece of data.

The example method depicted in FIG. 9 also includes retrieving (906),from object storage of the cloud-based storage system, the data that wasstored in the portion of the block storage of the cloud-based storagesystem that has become unavailable. Retrieving (906) the data that wasstored in the portion of the block storage of the cloud-based storagesystem that has become unavailable from object storage of thecloud-based storage system may be carried out, for example, through theuse of metadata described above that maps the data that was stored inthe portion of the block storage of the cloud-based storage system thathas become unavailable to one or more objects stored in the objectstorage of the cloud-based storage system that contain the piece ofdata. In such an example, retrieving (906) the data may be carried outby reading the objects that map to the data from the object storage ofthe cloud-based storage system.

The example method depicted in FIG. 9 also includes storing (908), inblock storage of the cloud-based storage system, the retrieved data.Storing (908) the retrieved data in block storage of the cloud-basedstorage system may be carried out, for example, by creating replacementcloud computing instances with local storage and storing the data in thelocal storage of one or more of the replacement cloud computinginstances, as described in greater detail above.

For further explanation, FIG. 10 sets forth a flow chart illustrating anadditional example method of servicing I/O operations in a cloud-basedstorage system (604). The example method depicted in FIG. 10 is similarto the example method depicted in many of the figures above, as theexample method depicted in FIG. 10 also includes receiving (606) arequest to write data to the cloud-based storage system (604), storing(614) the data in block storage of the cloud-based storage system (604),and storing (618) the data in object storage of the cloud-based storagesystem (604).

In the example method depicted in FIG. 10, receiving (606) the requestto write data to the cloud-based storage system can include receiving(1002), by a storage controller application executing in a cloudcomputing instance, the request to write data to the cloud-basedstorage. The storage controller application that is executing in a cloudcomputing instance may be similar to the storage controller applicationsdescribed above and may be executing, for example, in an EC2 instance asdescribed above in greater detail. In fact, the cloud-based storagesystem (604) may actually include multiple EC2 instances or similarcloud computing instances, where multiple cloud computing instances areeach executing the storage controller application.

In the example method depicted in FIG. 10, storing (614), in blockstorage of the cloud-based storage system, the data can include issuing(1004), by the storage controller application executing in the cloudcomputing instance, an instruction to write the data to local storagewithin one or more cloud computing instances with local storage. The oneor more cloud computing instances with local storage may be similar tothe cloud computing instances with local storage that are describedabove. In the example method depicted in FIG. 10, the storage controllerapplication executing in the cloud computing instance may be coupled fordata communications with a plurality of cloud computing instances withlocal storage. In such a way, the storage controller application that isexecuting in the cloud computing instance may treat the plurality ofcloud computing instances with local storage as individual storagedevices, such that the storage controller application that is executingin the cloud computing instance may issue (1004) an instruction to writethe data to local storage within one or more cloud computing instanceswith local storage by issuing the same set of commands that the storagecontroller application would issue when writing data to a connectedstorage device. Readers will appreciate that because the storagecontroller application that is executing in the cloud computing instancemay be coupled for data communications with a plurality of cloudcomputing instances with local storage, the storage array controller maybe connected to multiple sources of block storage, the storage arraycontroller could only be connected to a single EBS volume if the storagearray controller were configured to use EBS as its block-storage.

In the example method depicted in FIG. 10, one or more of the pluralityof cloud computing instances with local storage may be coupled for datacommunications with a plurality of cloud computing instances that areeach executing the storage controller application. Readers willappreciate that in some embodiments, because there are a plurality ofcloud computing instances that are each executing the storage controllerapplication, a storage controller application that is executing on afirst cloud computing instance may serve as the primary controllerwhereas additional storage controller applications that are executing onadditional cloud computing instances may serve as the secondarycontrollers that can take over for the primary controller upon theoccurrence of some event (e.g., failure of the primary controller).

For further explanation, FIG. 11 sets forth a flow chart illustrating anadditional example method of servicing I/O operations in a cloud-basedstorage system (604). The example method depicted in FIG. 11 is similarto the example method depicted in many of the figures above, as theexample method depicted in FIG. 11 also includes receiving (606) arequest to write data to the cloud-based storage system (604), storing(614) the data in block storage of the cloud-based storage system (604),and storing (618) the data in object storage of the cloud-based storagesystem (604).

In the example method depicted in FIG. 11, storing (614), in blockstorage of the cloud-based storage system, the data can include writing(1102), into one or more blocks of the block storage, the data using ablock-level protocol. In the example method depicted in FIG. 11, theblock storage may be embodied as one or more block storage devices suchas NAND flash memory where data is stored in blocks that can each beused to store data of a maximum size (i.e., a block size). Data may bewritten (1102) to such storage devices using a block-level protocol suchas, for example, iSCSI, Fibre Channel and FCoE (Fibre Channel overEthernet), and so on. Readers will appreciate that by writing (1102) thedata into one or more blocks of the block storage using a block-levelprotocol, the data that is written to the block storage of thecloud-based storage system is therefore stored in blocks.

In the example method depicted in FIG. 11, storing (618), in objectstorage of the cloud-based storage system, the data can include writing(1104), into one or more objects in the object storage, the data usingan object-level protocol. In the example method depicted in FIG. 11, theobject storage may be configured to manage data as objects, as opposedto other storage architectures like file systems which manage data as afile hierarchy, and block storage which manages data as blocks. Suchobject storage can be implemented at the device level (object storagedevice), the system level, the interface level, or in some other way.Data may be written (1104) to the object storage using an object-levelprotocol such as, for example, the SCSI command set for Object StorageDevices, RESTful/HTTP protocols, AWS S3 APIs, the Cloud Data ManagementInterface for accessing cloud storage, and others. Readers willappreciate that by writing (1104) one or more objects into the objectstorage using an object-level protocol, the data that is written to theobject storage of the cloud-based storage system is therefore stored inobjects—rather than blocks as was the case in the preceding paragraph.

In the example method depicted in FIG. 11, for each block of data, thedata contained in a particular block may be written into a uniqueobject. Readers will appreciate that each object that is written (1104)to object storage may include includes the data itself, as well as itsassociated metadata and each object may be associated with a globallyunique identifier—rather than a file name and a file path, block number,and so on. As such, the data that is contained in a particular block maybe written into a unique object in the sense that the unique objectincludes the data itself, metadata associated with the data, and aglobally unique identifier. In such embodiments, the cloud-based storagesystem may therefore maintain a mapping from each block of data that isstored in the cloud-based storage system's block storage and each objectthat is stored in the cloud-based storage system's object storage. Insome embodiments, each object may include the data that is contained inmultiple blocks, but the data that is contained in multiple blocks needonly be stored in a single object.

For further explanation, FIG. 12 illustrates an example virtual storagesystem architecture 1200 in accordance with some embodiments. Thevirtual storage system architecture may include similar cloud-basedcomputing resources as the cloud-based storage systems described abovewith reference to FIGS. 4-11.

As described above with reference to FIGS. 1A-3E, in some embodiments ofa physical storage system, a physical storage system may include one ormore controllers providing storage services to one or more hosts, andwith the physical storage system including durable storage devices, suchas solid state drives or hard disks, and also including some fastdurable storage, such as NVRAM. In some examples, the fast durablestorage may be used for staging or transactional commits or for speedingup acknowledgement of operation durability to reduce latency for hostrequests.

Generally, fast durable storage is often used for intent logging, fastcompletions, or quickly ensuring transactional consistency, where such(and similar) purposes are referred to herein as staging memory.Generally, both physical and virtual storage systems may have one ormore controllers, and may have specialized storage components, such asin the case of physical storage devices, specialized storage devices.Further, in some cases, in physical and virtual storage systems, stagingmemory may be organized and reorganized in a variety of ways, such as inexamples described later. In some examples, in whatever way that memorycomponents or memory devices are constructed, generated, or organized,there may be a set of storage system logic that executes to implement aset of advertised storage services and that stores bulk data forindefinite durations, and there may also be some quantity of stagingmemory.

In some examples, controller logic that operates a physical storagesystem, such as physical storage systems 1A-3E, may be carried outwithin a virtual storage system by providing suitable virtual componentsto, individually or in the aggregate, serve as substitutes for hardwarecomponents in a physical storage system—where the virtual components areconfigured to operate the controller logic and to interact with othervirtual components that are configured to replace physical componentsother than the controller.

Continuing with this example, virtual components, executing controllerlogic, may implement and/or adapt high availability models used to keepa virtual storage system operating in case of failures. As anotherexample, virtual components, executing controller logic, may implementprotocols to keep the virtual storage system from losing data in theface of transient failures that may exceed what the virtual storagesystem may tolerate while continuing to operate.

In some implementations, and particularly with regard to the variousvirtual storage system architectures described with reference to FIGS.12-17, a computing environment may include a set of available,advertised constructs that are typical to cloud-based infrastructures asservice platforms, such as cloud infrastructures provided by Amazon WebServices™, Microsoft Azure™, and/or Google Cloud Platform™. In someimplementations, example constructs, and construct characteristicswithin such cloud platforms may include:

-   -   Compute instances, where a compute instance may execute or run        as virtual machines flexibly allocated to physical host servers;    -   Division of computing resources into separate geographic        regions, where computing resources may be distributed or divided        among separate, geographic regions, such that users within a        same region or same zone as a given cloud computing resource may        experience faster and/or higher bandwidth access as compared to        users in a different region or different zone than computing        resources;    -   Division of resources within geographic regions into        “availability” zones with separate availability and        survivability in cases of wide-scale data center outages,        network failures, power grid failures, administrative mistakes,        and so on. Further, in some examples, resources within a        particular cloud platform that are in separate availability        zones within a same geographic region generally have fairly high        bandwidth and reasonably low latency between each other;    -   Local instance storage, such as hard drives, solid-state drives,        rack-local storage, that may provide private storage to a        compute instance. Other examples of local instance storage are        described above with reference to FIGS. 4-11;    -   Block stores that are relatively high-speed and durable, and        which may be connected to a virtual machine, but whose access        may be migrated. Some examples include EBS (Elastic Block        Store™) in AWS, Managed Disks in Microsoft Azure™, and Compute        Engine persistent disks in Google Cloud Platform™. EBS in AWS        operates within a single availability zone, but is otherwise        reasonably reliable and available, and intended for long-term        use by compute instances, even if those compute instances can        move between physical systems and racks;    -   Object stores, such as Amazon S3™ or an object store using a        protocol derived from, compatible with S3, or that has some        similar characteristics to S3 (for example, Microsoft's Azure        Blob Storage™). Generally, object stores are very durable,        surviving widespread outages through inter-availability zone and        cross-geography replication;    -   Cloud platforms, which may support a variety of object stores or        other storage types that may vary in their combinations of        capacity prices, access prices, expected latency, expected        throughput, availability guarantees, or durability guarantees.        For example, in AWS™, Standard and Infrequent Access S3 storage        classes (referenced herein as standard and write-mostly storage        classes) differ in availability (but not durability) as well as        in capacity and access prices (with the infrequent access        storage tier being less expensive on capacity, but more        expensive for retrieval, and with 1/10th the expected        availability). Infrequent Access S3 also supports an even less        expensive variant that is not tolerant to complete loss of an        availability zone, which is referred to herein as a        single-availability-zone durable store. AWS further supports        archive tiers such as Glacier™ and Deep Glacier™ that provide        their lowest capacity prices, but with very high access latency        on the order of minutes to hours for Glacier, and up to 12 hours        with limits on retrieval frequency for Deep Glacier. Glacier and        Deep Glacier are referred to herein as examples of archive and        deep archive storage classes;    -   Databases, and often multiple different types of databases,        including high-scale key-value store databases with reasonable        durability (similar to high-speed, durable block stores) and        convenient sets of atomic update primitives. Some examples of        durable key-value databases include AWS DynamoDB™, Google Cloud        Platform Big Table™, and/or Microsoft Azure's CosmoDB™; and    -   Dynamic functions, such as code snippets that can be configured        to run dynamically within the cloud platform infrastructure in        response to events or actions associated with the configuration.        For example, in AWS, these dynamic functions are called AWS        Lambdas™, and Microsoft Azure and Google Cloud Platform refers        to such dynamic functions as Azure Functions™ and Cloud        Functions™, respectively.

In some implementations, local instance storage is not intended to beprovisioned for long-term use, and in some examples, local instancestorage may not be migrated as virtual machines migrate between hostsystems. In some cases, local instance storage may also not be sharedbetween virtual machines, and may come with few durability guaranteesdue to their local nature (likely surviving local power and softwarefaults, but not necessarily more wide spread failures). Further, in someexamples, local instance storage, as compared to object storage, may bereasonably inexpensive and may not be billed based on I/Os issuedagainst them, which is often the case with the more durable blockstorage services.

In some implementations, objects within object stores are easy to create(for example, a web service PUT operation to create an object with aname within some bucket associated with an account) and to retrieve (forexample, a web service GET operation), and parallel creates andretrievals across a sufficient number of objects may yield enormousbandwidth. However, in some cases, latency is generally very poor, andmodifications or replacement of objects may complete in unpredictableamounts of time, or it may be difficult to determine when an object isfully durable and consistently available across the cloud platforminfrastructure. Further, generally, availability, as opposed todurability, of object stores is often low, which is often an issue withmany services running in cloud environments.

In some implementations, as an example baseline, a virtual storagesystem may include one or more of the following virtual components andconcepts for constructing, provisioning, and/or defining a virtualstorage system built on a cloud platform:

-   -   Virtual controller, such as a virtual storage system controller        running on a compute instance within a cloud platform's        infrastructure or cloud computing environment. In some examples,        a virtual controller may run on virtual machines, in containers,        or on bare metal servers;    -   Virtual drives, where a virtual drive may be a specific storage        object that is provided to a virtual storage system controller        to represent a dataset; for example, a virtual drive may be a        volume or an emulated disk drive that within the virtual storage        system may serve analogously to a physical storage system        “storage device”. Further, virtual drives may be provided to        virtual storage system controllers by “virtual drive servers”;    -   Virtual drive servers may be implemented by compute instances,        where virtual drive servers may present storage, such as virtual        drives, out of available components provided by a cloud        platform, such as various types of local storage options, and        where virtual drive servers implement logic that provides        virtual drives to one or more virtual storage system        controllers, or in some cases, provides virtual drives to one or        more virtual storage systems.    -   Staging memory, which may be fast and durable, or at least        reasonably fast and reasonably durable, where reasonably durable        may be specified according to a durability metric, and where        reasonably fast may be specified according to a performance        metric, such as IOPS;    -   Virtual storage system dataset, which may be a defined        collection of data and metadata that represents coherently        managed content that represents a collection of file systems,        volumes, objects, and other similar addressable portions of        memory;    -   Object storage, which may provide back-end, durable object        storage to the staging memory. As illustrated in FIG. 12,        cloud-based object storage 432 may be managed by the virtual        drives 1210-1216;    -   Segments, which may be specified as medium-sized chunks of data.        For example, a segment may be defined to be within a range of 1        MB-64 MB, where a segment may hold a combination of data and        metadata; and    -   Virtual storage system logic, which may be a set of algorithms        running at least on the one or more virtual controllers 408,        410, and in some cases, with some virtual storage system logic        also running on one or more virtual drives 1210-1216.

In some implementations, a virtual controller may take in or receive I/Ooperations and/or configuration requests from client hosts 1260, 1262(possibly through intermediary servers, not depicted) or fromadministrative interfaces or tools, and then ensure that I/O requestsand other operations run through to completion.

In some examples, virtual controllers may present file systems,block-based volumes, object stores, and/or certain kinds of bulk storagedatabases or key/value stores, and may provide data services such assnapshots, replication, migration services, provisioning, hostconnectivity management, deduplication, compression, encryption, securesharing, and other such storage system services.

In the example virtual storage system 1200 architecture illustrated inFIG. 12, a virtual storage system 1200 includes two virtual controllers,where one virtual controller is running within one time zone, time zone1251, and another virtual controller is running within another timezone, time zone 1252. In this example, the two virtual controllers aredepicted as, respectively, storage controller application 408 runningwithin cloud computing instance 404 and storage controller application410 running within cloud computing instance 406.

In some implementations, a virtual drive server, as discussed above, mayrepresent to a host something similar to physical storage device, suchas a disk drive or a solid-state drive, where the physical storagedevice is operating within the context of a physical storage system.

However, while in this example, the virtual drive presents similarly toa host as a physical storage device, the virtual drive is implemented bya virtual storage system architecture where the virtual storage systemarchitecture may be any of those depicted among FIGS. 4-16. Further, incontrast to virtual drives that have as an analog a physical storagedevice, as implemented within the example virtual storage systemarchitectures, a virtual drive server, may not have an analog within thecontext of a physical storage system. Specifically, in some examples, avirtual drive server may implement logic that goes beyond what istypical of storage devices in physical storage systems, and may in somecases rely on atypical storage system protocols between the virtualdrive server and virtual storage system controllers that do not have ananalog in physical storage systems. However, conceptually, a virtualdrive server may share similarities to a scale-out shared-nothing orsoftware-defined storage systems.

In some implementations, with reference to FIG. 12, the respectivevirtual drive servers 1210-1216 may implement respective softwareapplications or daemons 1230-1236 to provide virtual drives whosefunctionality is similar or even identical to that of a physical storagedevice—which allows for greater ease in porting storage system softwareor applications that are designed for physical storage systems. Forexample, they could implement a standard SAS, SCSI or NVMe protocol, orthey could implement these protocols but with minor or significantnon-standard extensions.

In some implementations, with reference to FIG. 12, staging memory maybe implemented by one or more virtual drives 1210-1216, where the one ormore virtual drives 1210-1216 store data within respective block-storevolumes 1240-1246 and local storage 1220-1226. In this example, theblock storage volumes may be AWS EBS volumes that may be attached, oneafter another, as depicted in FIG. 12, to two or more other virtualdrives. As illustrated in FIG. 12, block storage volume 1240 is attachedto virtual drive 1212, block storage volume 1242 is attached to virtualdrive 1214, and so on.

In some implementations, a segment may be specified to be part of anerasure coded set, such as based on a RAID-style implementation, where asegment may store calculated parity content based on erasure codes (e.g.RAID-5 P and Q data) computed from content of other segments. In someexamples, contents of segments may be created once, and after thesegment is created and filled in, not modified until the segment isdiscarded or garbage collected.

In some implementations, virtual storage system logic may also run fromother virtual storage system components, such as dynamic functions.Virtual storage system logic may provide a complete implementation ofthe capabilities and services advertised by the virtual storage system1200, where the virtual storage system 1200 uses one or more availablecloud platform components, such as those described above, to implementthese services reliably and with appropriate durability.

While the example virtual storage system 1200 illustrated in FIG. 12includes two virtual controllers, more generally, other virtual storagesystem architectures may have more or fewer virtual controllers, asillustrated in FIGS. 13-16. Further, in some implementations, andsimilar to the physical storage systems described in FIGS. 1A-4, avirtual storage system may include an active virtual controller and oneor more passive virtual controllers.

For further explanation, FIG. 13 illustrates an example virtual storagesystem architecture 1300 in accordance with some embodiments. Thevirtual storage system architecture may include similar cloud-basedcomputing resources as the cloud-based storage systems described abovewith reference to FIGS. 4-12.

In this implementation, a virtual storage system may run virtual storagesystem logic, as specified above with reference to FIG. 12, concurrentlyon multiple virtual controllers, such as by dividing up a dataset or bycareful implementation of concurrent distributed algorithms. In thisexample, the multiple virtual controllers 1320, 408, 410, 1322 areimplemented within respective cloud computing instances 1310, 404, 406,1312.

As described above with reference to FIG. 12, in some implementations, aparticular set of hosts may be directed preferentially or exclusively toa subset of virtual controllers for a dataset, while a particulardifferent set of hosts may be directed preferentially or exclusively toa different subset of controllers for that same dataset. For example,SCSI ALUA (Asymmetric Logical Unit Access), or NVMe ANA (AsymmetricNamespace Access) or some similar mechanism, could be used to establishpreferred (sometimes called “optimized”) path preferences from one hostto a subset of controllers where traffic is generally directed to thepreferred subset of controllers but where, such as in the case offaulted requests or network failures or virtual storage systemcontroller failures, that traffic could be redirected to a differentsubset of virtual storage system controllers. Alternately, SCSI/NVMevolume advertisements or network restrictions, or some similaralternative mechanism, could force all traffic from a particular set ofhosts exclusively to one subset of controllers, or could force trafficfrom a different particular set of hosts to a different subset ofcontrollers.

As illustrated in FIG. 13, a virtual storage system may preferentiallyor exclusively direct I/O requests from host 1260 to virtual storagecontrollers 1320 and 408 with storage controllers 410 and perhaps 1322potentially being available to host 1260 for use in cases of faultedrequests, and may preferentially or exclusively direct I/O requests fromhost 1262 to virtual storage controllers 410 and 1322 with storagecontrollers 408 and perhaps 1320 potentially being available to host12622 for use in cases of faulted requests. In some implementations, ahost may be directed to issue I/O requests to one or more virtualstorage controllers within the same availability zone as the host, withvirtual storage controllers in a different availability zone from thehost being available for use in cases of faults.

For further explanation, FIG. 14 illustrates an example virtual storagesystem architecture 1400 in accordance with some embodiments. Thevirtual storage system architecture may include similar cloud-basedcomputing resources as the cloud-based storage systems described abovewith reference to FIGS. 4-13.

In some implementations, boundaries between virtual controllers andvirtual drive servers that host virtual drives may be flexible. Further,in some examples, the boundaries between virtual components may not bevisible to client hosts 1450 a-1450 p, and client hosts 1450 a-1450 pmay not detect any distinction between two differently architectedvirtual storage systems that provides a same set of storage systemservices.

For example, virtual controllers and virtual drives may be merged into asingle virtual entity that may provide similar functionality to atraditional, blade-based scale-out storage system. In this example,virtual storage system 1400 includes n virtual blades, virtual blades1402 a-1402 n, where each respective virtual blade 1402 a-1402 n mayinclude a respective virtual controller 1404 a-1404 n, and also includerespective local storage 1220-1226, 1240-1246, but where the storagefunction may make use of a platform provided object store as might bethe case with virtual drive implementations described previously.

In some implementations, because virtual drive servers support generalpurpose compute, this virtual storage system architecture supportsfunctions migrating between virtual storage system controllers andvirtual drive servers. Further, in other cases, this virtual storagesystem architecture supports other kinds of optimizations, such asoptimizations described above that may be performed within stagingmemory. Further, virtual blades may be configured with varying levels ofprocessing power, where the performance specifications of a given one ormore virtual blades may be based on expected optimizations to beperformed.

For further explanation, FIG. 15 illustrates an example virtual storagesystem architecture 1500 in accordance with some embodiments. Thevirtual storage system architecture may include similar cloud-basedcomputing resources as the cloud-based storage systems described abovewith reference to FIGS. 4-14.

In this implementations, a virtual storage system 1500 may be adapted todifferent availability zones, where such a virtual storage system 1500may use cross-storage system synchronous replication logic to isolate asmany parts of an instance of a virtual storage system as possible withinone availability zone. For example, the presented virtual storage system1500 may be constructed from a first virtual storage system 1502 in oneavailability zone, zone 1, that synchronously replicates data to asecond virtual storage system 1504 in another availability zone, zone 2,such that the presented virtual storage system can continue running andproviding its services even in the event of a loss of data oravailability in one availability zone or the other. Such animplementation could be further implemented to share use of durableobjects, such that the storing of data into the object store iscoordinated so that the two virtual storage systems do not duplicate thestored content. Further, in such an implementation, the twosynchronously replicating storage systems may synchronously replicateupdates to the staging memories and perhaps local instance stores withineach of their availability zones, to greatly reduce the chance of dataloss, while coordinating updates to object stores as a laterasynchronous activity to greatly reduce the cost of capacity stored inthe object store.

In this example, virtual storage system 1504 is implemented within cloudcomputing environments 1501. Further, in this example, virtual storagesystem 1502 may use cloud-based object storage 1550, and virtual storagesystem 1504 may use cloud-based storage 1552, where in some cases, suchas AWS S3, the different object storages 1550, 1552 may be a same cloudobject storage with different buckets.

Continuing with this example, virtual storage system 1502 may, in somecases, synchronously replicate data to other virtual storage systems, orphysical storage systems, in other availability zones (not depicted).

In some implementations, the virtual storage system architecture ofvirtual storage systems 1502 and 1504 may be distinct, and evenincompatible—where synchronous replication may depend instead onsynchronous replication models being protocol compatible. Synchronousreplication is described in greater detail above with reference to FIGS.3D and 3E.

In some implementations, virtual storage system 1502 may be implementedsimilarly to virtual storage system 1400, described above with referenceto FIG. 14, and virtual storage system 1504 may be implemented similarlyto virtual storage system 1200, described above with reference to FIG.12.

For further explanation, FIG. 16 illustrates an example virtual storagesystem architecture 1500 in accordance with some embodiments. Thevirtual storage system architecture may include similar cloud-basedcomputing resources as the cloud-based storage systems described abovewith reference to FIGS. 4-15.

In some implementations, similar to the example virtual storage system1500 described above with reference to FIG. 15, a virtual storage system1600 may include multiple virtual storage systems 1502, 1504 thatcoordinate to perform synchronous replication from one virtual storagesystem to another virtual storage system.

However, in contrast to the example virtual storage system 1500described above, the virtual storage system 1600 illustrated in FIG. 16provides a single cloud-based object storage 1650 that is shared amongthe virtual storage systems 1502, 1504.

In this example, the shared cloud-based object storage 1650 may betreated as an additional data replica target, with delayed updates usingmechanisms and logic associated with consistent, but non-synchronousreplication models. In this way, a single cloud-based object storage1650 may be shared consistently between multiple, individual virtualstorage systems 1502, 1504 of a virtual storage system 1600.

In each of these example virtual storage systems, virtual storage systemlogic may generally incorporate distributed programming concepts tocarry out the implementation of the core logic of the virtual storagesystem. In other words, as applied to the virtual storage systems, thevirtual system logic may be distributed between virtual storage systemcontrollers, scale-out implementations that combine virtual systemcontrollers and virtual drive servers, and implementations that split orotherwise optimize processing between the virtual storage systemcontrollers and virtual drive servers.

For further explanation, FIG. 17 sets forth a flow chart illustrating anexample method of data flow within in a virtual storage system 1700. Theexample method depicted in FIG. 17 may be implemented on any of thevirtual storage systems described above with reference to FIGS. 12-16.In other words, virtual storage system 1700 may be implemented by eithervirtual storage system 1200, 1300, 1400, 1500, or 1600.

As depicted in FIG. 17, the example method includes receiving (1702), bya virtual storage system 1700, a request to write data to the virtualstorage system 1700; storing (1704), within staging memory provided byone or more virtual drives of the virtual storage system 1700, the data1754; and migrating (1706), from the staging memory to more durable datastorage provided by a cloud service provider, at least a portion of datastored within the staging memory.

Receiving (1702), by the virtual storage system 1700, the request towrite data to the virtual storage system 1700 may be carried out asdescribed above with reference to FIGS. 4-16, where the data may beincluded within one or more received storage operations 1752, and therequest may be received using one or more communication protocols, orone or more API calls provided by a cloud computing environment 402 thatis hosting the virtual storage system 1700.

Storing (1704), within staging memory provided by one or more virtualdrives of the virtual storage system 1700, the data 1754 may be carriedout as described above with reference to virtual storage systems1200-1600, where a virtual storage system, for example, virtual storagesystem 1200, receives data from a client host 1260 at a virtualcontroller 408, 410, and where the virtual controller 408, 410 storesthe data among the local storage of the layer of virtual drives1210-1216. Staging memory provided by virtual drives is described ingreater detail above with reference to FIG. 12.

Migrating (1706), from the staging memory to more durable data storageprovided by a cloud service provider, at least a portion of data storedwithin the staging memory may be carried out as described above withreference to FIGS. 4-16, where data is migrated from staging memory to acloud-based object storage.

Additional examples of receiving data and storing the data withinstaging memory, and subsequently migrating data from staging memory tomore durable storage are described within co-pending patent applicationSer. No. 16/524,861, which is incorporated in its entirely for allpurposes herein. Specifically, all of the migration techniques describedin co-pending patent application Ser. No. 16/524,861, which describestoring data within staging memory, also referred to as a first tier ofstorage, and optionally processing, modifying, or optimizing the datawithin the staging memory before, based on a migration event, thestaging memory data is migrated to more durable memory, or cloud-basedobject storage.

For further explanation, FIG. 18 sets forth a flow chart illustrating anexample method of data flow within in a virtual storage system 1700. Theexample method depicted in FIG. 18 may be implemented by one any of thevirtual storage systems described above with reference to FIGS. 4-16. Inother words, virtual storage system 1700 may be implemented at least byeither virtual storage system 1200, 1300, 1400, 1500, 1502, 1504, or1600, either individually or by a combination of individual features.

The above example with regard to FIG. 18 describes an implementation ofdata flow through storage tiers of a virtual storage system, and morespecifically, data flowing from staging memory to more durable objectstorage. However, more generally, data flow through a virtual storagesystem may occur in stages between any pair of multiple, different tiersof storage. Specifically, in this example, different tiers of storagemay be: (1) virtual controller storage, (2) staging memory fortransactional consistency and fast completions, (3) storage withinvirtual drives provided by virtual drive servers, (4) virtual driveserver local instance store(s), and (5) an object store that is providedby a cloud services provider.

As depicted in FIG. 18, the example method includes: receiving (1802),by a virtual storage system 1700, a request to write data to the virtualstorage system 1700; storing (1804), within storage provided by a firsttier of storage of the virtual storage system 1700, the data 1854; andmigrating (1806), from the first tier of storage to a second tier ofstorage, at least a portion of data stored within the first tier ofstorage.

Receiving (1802), by the virtual storage system 1700, the request towrite data 1854 to the virtual storage system 1700 may be carried out asdescribed above with reference to FIGS. 4-17, where the data may beincluded within one or more received storage operations 1852 from a hostcomputer or application, and the request may be received using one ormore communication protocols, or one or more API calls provided by acloud computing environment 402 that is hosting the virtual storagesystem 1700.

Storing (1804), within storage provided by a first tier of storage ofthe virtual storage system 1700, the data 1854 may be carried out asdescribed above with reference to FIGS. 4-17, where one or more virtualcontrollers may be configured to receive and handle storage operations1852, including processing write requests and storing correspondingwrite data into one or more storage tiers of the virtual storage system1700. Five example storage tiers of the virtual storage system aredescribed above, with reference to the beginning description for FIG.18.

Migrating (1806), from the first tier of storage to a second tier ofstorage, at least a portion of data stored within the first tier ofstorage may be carried as described above with regard to movement ofdata through various tiers of storage. Further, in some examples, asdescribed above, data may be transformed in various ways at one or ofthe storage tiers, including deduplication, overwriting, aggregatinginto segments, among other transformations, generating recovery metadataor continuous-data-protection metadata, as data flows from the one ormore virtual controllers through the virtual storage system 1700 intobackend storage, including one or more of object storage and any of thestorage class options described below.

A virtual storage system may dynamically adjust cloud platform resourceusage in response to changes in cost requirements based upon cloudplatform pricing structures, as described in greater detail below.

Under various conditions, budgets, capacities, usage and/or performanceneeds may change, and a user may be presented with cost projections anda variety of costing scenarios that may include increasing a number ofserver or storage components, the available types of components, theplatforms that may provide suitable components, and/or models for bothhow alternatives to a current setup might work and cost in the future.In some examples, such cost projections may include costs of migratingbetween alternatives given that network transfers incur a cost, wheremigrations tend to include administrative overhead, and for a durationof a transfer of data between types of storage or vendors, additionaltotal capacity may be needed until necessary services are fullyoperational.

Further, in some implementations, instead of pricing out what is beingused and providing options for configurations based on potential costs,a user may, instead, provide a budget, or otherwise specify an expensethreshold, and the storage system service may generate a virtual storagesystem configuration with specified resource usage such that the storagesystem service operates within the budget or expense threshold.

Continuing with this example of a storage system service operatingwithin a budget or expense threshold—with regard to compute resources,while limiting compute resources limits performance, costs may bemanaged based on modifying configurations of virtual applicationservers, virtual storage system controllers, and other virtual storagesystem components by adding, removing, or replacing with faster orslower virtual storage system components. In some examples, if costs orbudgets are considered over given lengths of time, such as monthly,quarterly, or yearly billing, then by ratcheting down the cost ofvirtual compute resources in response to lowered workloads, more computeresources may be available in response to increases in workloads.

Further, in some examples, in response to determining that givenworkloads may be executed at flexible times, those workloads may bescheduled to execute during periods of time that are less expensive tooperate or initiate compute resources within the virtual storage system.In some examples, costs and usage may be monitored over the course of abilling period to determine whether usage earlier in the billing periodmay affect the ability to run at expected or acceptable performancelevels later in the billing period, or whether lower than expected usageduring parts of a billing period suggest there is sufficient budgetremaining to run optional work or to suggest that renegotiating termswould reduce costs.

Continuing with this example, such a model of dynamic adjustments to avirtual storage system in response to cost or resource constraints maybe extend from compute resources to also include storage resources.However, a different consideration for storage resources is that storageresources have less elastic costs than compute resources because storeddata continues to occupy storage resources over a given period of time.

Further, in some examples, there may be transfer costs within cloudplatforms associated with migrating data between storage services thathave different capacity and transfer prices. Each of these costs ofmaintaining virtual storage system resources must be considered and mayserve as a basis for configuring, deploying, and modifying computeand/or storage resources within a virtual storage system.

In some cases, the virtual storage system may adjust in response tostorage costs based on cost projections that may include comparingcontinuing storage costs using existing resources as compared to acombination of transfer costs of the storage content and storage costsof less expensive storage resources (such as storage provided by adifferent cloud platform, or to or from storage hardware incustomer-managed data centers, or to or from customer-managed hardwarekept in a collocated shared management data center). In this way, over agiven time span that is long enough to support data transfers, and insome cases based on predictable use patterns, a budget limit-basedvirtual storage system model may adjust in response to different cost orbudget constraints or requirements.

In some implementations, as capacity grows in response to anaccumulation of stored data, and as workloads, over a period of time,fluctuate around some average or trend line, a dynamically configurablevirtual storage system may calculate whether a cost of transferring anamount of data to some less expensive type of storage class or lessexpensive location of storage may be possible within a given budget orwithin a given budget change. In some examples, the virtual storagesystem may determine storage transfers based on costs over a period oftime that includes a billing cycle or multiple billing cycles, and inthis way, preventing a budget or cost from being exceeded in asubsequent billing cycle.

In some implementations, a cost managed or cost constrained virtualstorage system, in other words, a virtual storage system thatreconfigures itself in response to cost constraints or other resourceconstraints, may also make use of write-mostly, archive, or deep archivestorage classes that are available from cloud infrastructure providers.Further, in some cases, the virtual storage system may operate inaccordance with the models and limitations described elsewhere withregard to implementing a storage system to work with differentlybehaving storage classes.

For example, a virtual storage system may make automatic use of awrite-mostly storage class based on a determination that a cost orbudget may be saved and reused for other purposes if data that isdetermined to have a low likelihood of access is consolidated, such asinto segments that consolidate data with similar access patterns orsimilar access likelihood characteristics.

Further, in some cases, consolidated segments of data may then bemigrated to a write-mostly storage class, or other lower cost storageclass. In some examples, use of local instance stores on virtual drivesmay result in cost reductions that allow virtual storage system resourceadjustments that result in reducing costs to satisfy cost or budgetchange constraints. In some cases, the local instance stores may usewrite-mostly object stores as a backend, and because read load is oftentaken up entirely by the local instance stores, the local instancestores may operate mostly as a cache rather than storing complete copiesof a current dataset.

In some examples, a single-availability, durable store may also be usedif a dataset may be identified that is not required or expected tosurvive loss of an availability zone, and such use may serve as a costsavings basis in dynamically reconfiguring a virtual storage system. Insome cases, use of a single-availability zone for a dataset may includean explicit designation of the dataset, or indirect designation throughsome storage policy.

Further, the designation or storage policy may also include anassociation with a specific availability zone; however, in some cases,the specific availability zone may be determined by a datasetassociation with, for example, host systems that are accessing a virtualstorage system from within a particular availability zone. In otherwords, in this example, the specific availability zone may be determinedto be a same availability zone that includes a host system.

In some implementations, a virtual storage system may base a dynamicreconfiguration on use of archive or deep archive storage classes, ifthe virtual storage system is able to provide or satisfy performancerequirements while storage operations are limited by the constraints ofarchive and/or deep archive storage classes. Further, in some cases,transfer of old snapshot or continuous data protection datasets, orother datasets that are no longer active, may be enabled to betransferred to archive storage classes based on a storage policyspecifying a data transfer in response to a particular activity level,or based on a storage policy specify a data transfer in response to datanot being accessed for a specified period of time. In other examples,the virtual storage system may transfer data to an archive storage classin response to a specific user request.

Further, given that retrieval from an archive storage class may takeminutes, hours, or days, users of the particular dataset being stored inan archive or deep archive storage class may be requested by the virtualstorage system to provide specific approval of the time required toretrieve the dataset. In some examples, in the case of using deeparchive storage classes, there may also be limits on how frequently dataaccess is allowed, which may put further constraints on thecircumstances in which the dataset may be stored in archive or deeparchive storage classes.

Implementing a virtual storage system to work with differently behavingstorage classes may be carried out using a variety of techniques, asdescribed in greater detail below.

In various implementations, some types of storage, such as awrite-mostly storage class may have lower prices for storing and keepingdata than for accessing and retrieving data. In some examples, if datamay be identified or determined to be rarely retrieved, or retrievedbelow a specified threshold frequency, then costs may be reduced bystoring the data within a write-mostly storage class. In some cases,such a write-mostly storage class may become an additional tier ofstorage that may be used by virtual storage systems with access to oneor more cloud infrastructures that provide such storage classes.

For example, a storage policy may specify that a write-mostly storageclass, or other archive storage class, may be used for storing segmentsof data from snapshots, checkpoints, or historical continuous dataprotection datasets that have been overwritten or deleted from recentinstances of the datasets they track. Further, in some cases, thesesegments may be transferred based on exceeding a time limit withoutbeing accessed, where the time limit may be specified in a storagepolicy, and where the time limit corresponds to a low likelihood ofretrieval—outside of inadvertent deletion or corruption that may requireaccess to an older historical copy of a dataset, or a fault orlarger-scale disaster that may require some forensic investigation, acriminal event, an administrative error such as inadvertently deletingmore recent data or the encryption or deletion or a combination of partsor all of a dataset and its more recent snapshots, clones, or continuousdata protection tracking images as part of a ransomware attack.

In some implementations, use of a cloud-platform write-mostly storageclass may create cost savings that may then be used to provision computeresources to improve performance of the virtual storage system. In someexamples, if a virtual storage system tracks and maintains storageaccess information, such as using an age andsnapshot/clone/continuous-data-protection-aware garbage collector orsegment consolidation and/or migration algorithm, then the virtualstorage system may use a segment model as part of establishing efficientmetadata references while minimizing an amount of data transferred tothe mostly-write storage class.

Further, in some implementations, a virtual storage system thatintegrates snapshots, clones, or continuous-data-protection trackinginformation may also reduce an amount of data that may be read back froma write-mostly storage repository as data already resident in lessexpensive storage classes, such as local instance stores on virtualdrives or objects stored in a cloud platform's standard storage class,may be used for data that is still available from these local storagesources and has not been overwritten or deleted since the time of asnapshot, clone, or continuous-data-protection recovery point havingbeen written to write-mostly storage. Further, in some examples, dataretrieved from a write-mostly storage class may be written into someother storage class, such as virtual drive local instance stores, forfurther use, and in some cases, to avoid being charged again forretrieval.

In some implementations, an additional level of recoverable content maybe provided based on the methods and techniques described above withregard to recovering from loss of staging memory content, where theadditional level of recoverable content may be used to providereliability back to some consistent points in the past entirely fromdata stored in one of these secondary stores including objects stored inthese other storage classes.

Further, in this example, recoverability may be based on recording theinformation necessary to roll back to some consistent point, such as asnapshot or checkpoint, using information that is held entirely withinthat storage class. In some examples, such an implementation may bebased on a storage class including a complete past image of a datasetinstead of only data that has been overwritten or deleted, whereoverwriting or deleting may prevent data from being present in morerecent content from the dataset. While this example implementation mayincrease costs, as a result, the virtual storage system may provide avaluable service such as recovery from a ransomware attack, whereprotection from a ransomware attack may be based on requiring additionallevels of permission or access that restrict objects stored in the givenstorage class from being deleted or overwritten.

In some implementations, in addition to or instead of using awrite-mostly storage class, a virtual storage system may also usearchive storage classes and/or deep archive storage classes for contentthat is—relative to write-mostly storage classes—even less likely to beaccessed or that may only be needed in the event of disasters that areexpected to be rare, but for which a high expense is worth the abilityto retrieve the content. Examples of such low access content may includehistorical versions of a dataset, or snapshots, or clones that may, forexample, be needed in rare instances, such as a discovery phase inlitigation or some other similar disaster, particularly if another partymay be expected to pay for retrieval.

However, as noted above, keeping historical versions of a dataset, orsnapshots, or clones in the event of a ransomware attack may be anotherexample. In some examples, such as the event of litigation, and toreduce an amount of data stored, a virtual storage system may only storeprior versions of data within datasets that have been overwritten ordeleted. In other examples, such as in the event of ransomware ordisaster recovery, as described above, a virtual storage system maystore a complete dataset in archive or deep archive storage class, inaddition to storing controls to eliminate the likelihood of unauthorizeddeletions or overwrites of the objects stored in the given archive ordeep archive storage class, including storing any data needed to recovera consistent dataset from at least a few different points in time.

In some implementations, a difference between how a virtual storagesystem makes use of: (a) objects stored in a write-mostly storage classand (b) objects stored in archive or deep archive storage classes, mayinclude accessing a snapshot, clone, or continuous-data-protectioncheckpoint that accesses a given storage class. In the example of awrite-mostly storage class, objects may be retrieved with a similar, orperhaps identical, latency to objects stored in a standard storage classprovided by the virtual storage system cloud platform, where the costfor storage in the write-mostly storage class may be higher than thestandard storage class.

In some examples, a virtual storage system may implement use of thewrite-mostly storage class as a minor variant of a regular model foraccessing content that correspond to segments only currently availablefrom objects in the standard storage class. In particular, in thisexample, data may be retrieved when some operation is reading that data,such as by reading from a logical offset of a snapshot of a trackingvolume. In some cases, a virtual storage system may request agreementfrom a user to pay extra fees for any such retrievals at the time accessto the snapshot, or other type of stored image, is requested, and theretrieved data may be stored into local instance stores associated witha virtual drive or copied (or converted) into objects in a standardstorage class to avoid continuing to pay higher storage retrieval feesusing the other storage class that is not included within thearchitecture of the virtual storage system.

In some implementations, in contrast to the negligible latencies inwrite-mostly storage classes discussed above, latencies or proceduresassociated with retrieving objects from archive or deep archive storageclasses may make implementation impractical. In some cases, if itrequires hours or days to retrieve objects from an archive or deeparchive storage class, then an alternative procedure may be implemented.For example, a user may request access to a snapshot that is known torequire at least some segments stored in objects stored in an archive ordeep archive storage class, and in response, instead of reading any suchsegments on demand, the virtual storage system may determine a list ofsegments that include the requested dataset (or snapshot, clone, orcontinuous data protection recovery point) and that are stored intoobjects in the archive or deep archive storage.

In this way, in this example, the virtual storage system may requestthat the segments in the determined list of segments be retrieved to becopied into, say, objects in a standard storage class or into virtualdrives to be stored in local instance stores. In this example, theretrieval of the list of segments may take hours or days, but from aperformance and cost basis, it is preferable to request the entire listof segments at once instead of making individual requests on demand.Finishing with this example, after the list of segments has beenretrieved from the archive or deep archive storage, then access may beprovided to the retrieved snapshot, clone, or continuous data protectionrecovery point.

Readers will appreciate that although the embodiments described aboverelate to embodiments in which data that was stored in the portion ofthe block storage of the cloud-based storage system that has becomeunavailable is essentially brought back into the block-storage layer ofthe cloud-based storage system by retrieving the data from the objectstorage layer of the cloud-based storage system, other embodiments arewithin the scope of the present disclosure. For example, because datamay be distributed across the local storage of multiple cloud computinginstances using data redundancy techniques such as RAID, in someembodiments the lost data may be brought back into the block-storagelayer of the cloud-based storage system through a RAID rebuild.

Readers will further appreciate that although the preceding paragraphsdescribe cloud-based storage systems and the operation thereof, thecloud-based storage systems described above may be used to offer blockstorage as-a-service as the cloud-based storage systems may be spun upand utilized to provide block service in an on-demand, as-neededfashion. In such an example, providing block storage as a service in acloud computing environment, can include: receiving, from a user, arequest for block storage services; creating a volume for use by theuser; receiving I/O operations directed to the volume; and forwardingthe I/O operations to a storage system that is co-located with hardwareresources for the cloud computing environment.

For further explanation, FIG. 19 illustrates an example virtual storagesystem 1900 architecture in accordance with some embodiments. Thevirtual storage system architecture may include similar virtualcomponents and architectures as the cloud-based storage systemsdescribed above with reference to FIGS. 4-18. However, the virtualstorage system 1900 architecture depicted in FIG. 19 is an on-premisesvirtual storage system provisioned in a virtual environment 1902supported by on-premises physical storage resources. Here, “on-premises”refers to physical storage resources owned or leased by an enterprise ororganization and located in a private data center, as opposed tocloud-based storage resources provided in a public cloud infrastructureby a cloud services provider. While an on-premises virtual storagesystem is distinguishable from a cloud-based virtual storage system inthat the configuration of the underlying physical storage resources maybe serviced, managed, and administered by the enterprise personnel, thevirtual environment 1902 may itself be a cloud computing environmentsuch as a private cloud platform that presents an abstraction of theon-premises physical resources. Accordingly, the management andconfiguration of storage services provided by the on-premises virtualstorage system 1900 may be divorced from the management andconfiguration of the physical on-premises resources that host thevirtual storage system 1900, thus allowing the on-premises virtualstorage system to be administered in the same manner and using the sameinterfaces as it would be if it were provisioned on resources providedby a cloud service provider. As will be explained in greater detailbelow, the virtual environment 1902 hosted on on-premises resourcesallows the virtual components of the virtual storage system 1900 to bereplicated to or reconstructed in the cloud computing environment (orthe reverse), for example, to facilitate scale-out of the virtualstorage system, migration of the virtual storage system, and movement ofa virtual storage system dataset between an on-premises virtual storagesystem and a cloud-based virtual storage system.

In the example depicted in FIG. 19, the virtual storage system 1900includes one or more virtual controllers that are implemented in one ormore compute instances, where a compute instance may execute or run asvirtual machines flexibly allocated to on-premises physical hostservers. Like the virtual controllers 408, 406, a virtual controller maytake in or receive I/O operations and/or configuration requests fromclient hosts 1260, 1262 (possibly through intermediary servers, notdepicted) or from administrative interfaces or tools, and then ensurethat I/O requests and other operations run through to completion. Insome examples, virtual controllers may present file systems, block-basedvolumes, object stores, and/or certain kinds of bulk storage databasesor key/value stores, and may provide data services such as snapshots,replication, migration services, provisioning, host connectivitymanagement, deduplication, compression, encryption, secure sharing, andother such storage system services.

In the example depicted in FIG. 19, two virtual controllers are depictedas, respectively, storage controller application 1908 running withincompute instance 1904 and storage controller application 1910 runningwithin compute instance 1906. The compute instances 1904, 1906 mayexecute on virtual machines within the virtual environment 1902 thathosted on the on-premises physical resources. For example, multiplecompute instances running the storage controller application may behosted on disparate servers within one or more data centers, such that,in the event of a fault in one server, the storage controllerapplication in a compute instance hosted on a different server maycontinue to service storage operations directed to the virtual storagesystem.

In the example depicted in FIG. 19, the virtual storage system 1900includes one or more virtual drives 1910-1916 that are implemented inone or more compute instances, where a compute instance may execute orrun as virtual machines flexibly allocated to on-premises physical hostservers. Analogous to the virtual drives 1210-1216, the virtual drives1910-1916 provide persistent storage (e.g., block-level storage, objectstorage) to virtual controllers such as the storage controllerapplications 1908, 1910. In some implementations, staging memory may beimplemented by one or more virtual drives 1910-1916, where the one ormore virtual drives 1910-1916 store data, for example, within and localstorage 1920-1926. In some examples, the local storage 1920-1926 may beone or more SSDs of the respective on-premises physical resource hostingthe compute instance in which the virtual drive is implemented, or asother forms of storage devices such as one or more direct flash modules.In some implementations the contents of the local storage 1920-1926 ofone or more virtual drives 1910-1916 may be replicated or mirroredacross multiple virtual drives for data recovery and high availabilityof data. In additional implementations, data in the local storage1920-1926 of one or more virtual drives 1910-1916 may be striped acrossmultiple virtual drives 1910-1916 in a RAID configuration.

In some implementations, staging memory implemented by one or morevirtual drives (e.g., virtual drives 1910 and 1916 may store data withrespective block-store volumes 1940 and 1946. Readers will appreciatethat although the remainder of the description of FIG. 19 relates toembodiments in which the virtual drives 1910 and 1916 store data withinblock-store volumes 1940 and 1946, such a description is included onlyfor ease of explanation and does not represent a limitation as to thetype of storage (e.g., block storage, object storage, file storage) thatmay be offered by the virtual drives 1910-1916. Readers will alsoappreciate that virtual drives may or may not include block-storevolumes. In some implementations, the block storage volumes 1940 and1946 may be block storage volumes in one or more on-premises physicalstorage systems. The physical storage systems may operate as describedabove. For example, the physical storage systems may implementsynchronous replication such that one or more of the block storagevolumes 1940 and 1946 may be synchronously replicated across multiplephysical storage systems. In some implementations, the location andprovisioning of block storage volumes 1940 and 1946 within theon-premises resources is not visible to the host application or anadministrator of the storage services provided by the virtual storagesystem, such that the block storage volumes 1940 and 1946 may behavelike cloud-based block storage volumes (e.g., an Amazon EBS volume). Theblock storage volumes may be attached, one after another, as depicted inFIG. 12, to two or more other virtual drives. In some implementations,the block storage volume may be a cloud-based block storage volumeprovided by a cloud services provider (e.g., an AWS EBS volume).

In the example depicted in FIG. 19, the virtual drives 1910-1916 arecoupled to an object store, such as cloud-based object storage 432, thatprovides provide back-end, durable object storage. As illustrated inFIG. 19, committing data to the cloud-based object storage 432 may bemanaged by the virtual drives 1910-1916. In some implementations, asoftware daemon 1230-1236 or some other module of computer programinstructions that is executing on the virtual drive instance 1910-1916may be configured to not only write the data to its own local storage1920-1926 resources and any appropriate block storage 1940 and 1946 thatare offered by the virtual computing environment 1902, but the softwaredaemon 1230-1236 or some other module of computer program instructionsthat is executing on the particular virtual drive 1910-1916 may also beconfigured to write the data to cloud-based object storage 432 that isattached to the particular virtual drive. For example, data written tothe storage resources of the virtual drives 1910-1916 hosted on-premisesmay be automatically replicated to the cloud-based object storage, aspreviously discussed.

Readers will appreciate the on-premises virtual storage system 1900constructed utilizing the architecture set forth above allows a hostapplication or administrator to treat the on-premises virtual storagesystem 1900 as if it were a cloud-based virtual storage system, suchthat the virtual storage system 1900 allows a user to provision storageresources from multiple storage tiers based on performance anddurability characteristics while remaining agnostic to the configurationof the on-premises physical resources that are utilized to support thevirtual storage system. Readers will also appreciate that theon-premises virtual storage system 1900 can provide a set of storageservices and interfaces that are similar, if not identical, to acloud-based virtual storage system, thus facilitating interoperabilitybetween the on-premises storage resources and cloud-native applications.For example, the on-premises virtual storage system 1900 provides thesame set of virtual controllers, drive instances, block level storageservices, object storage services, and interfaces as those provided bythe cloud-based virtual storage systems depicted in FIG. 4-18. In oneexample, the same API used to construct the on-premises virtual storagesystem 1900 may be used to construct the cloud-based virtual storagesystems depicted in FIG. 4-18. Readers will also appreciate that theon-premises virtual storage system 1900 may be easily scaled out to acloud computing environment or migrated to and from the cloud computingenvironment; for example, in accordance with a cost model. For example,a virtual storage system service may spin up an instance of the virtualcontroller and/or an instance of a virtual drive in the cloud computingenvironment and connect those instances to the on-premises virtualstorage system 1900.

In some implementations, the on-premises virtual storage system 1900 maybe provided to a customer as a “cloud in a box” that includes thevirtual environment, hardware infrastructure, and storage resources forhosting the on-premises virtual storage system 1900. In this example,the on-premises virtual storage system 1900 may include VM templates forcreating the virtual machines that host the virtual controllers andvirtual drives. Likewise, the on-premises virtual storage system 1900may include a preinstalled storage controller application that iscompatible with a storage controller application used to manage otheron-premises physical resources such as an NFS or storage array. Byimplementing a storage controller application that may be hosted on acloud-based virtual storage system or an on-premises virtual storagesystem, and that is compatible with a storage controller application forphysical storage resources, a unified data experience may be provided tothe customer. Moreover, by providing on-premises virtual storage systemutilizing the customer's on-premises physical resources, the customermay allow its personnel to configure virtual storage systems as if theywere cloud-based storage systems (e.g., by setting quotas, creatingvolumes and other storage components, monitoring performance, definingaccess control, applying policies), while leaving the administration ofthe physical environment (e.g., provisioning virtual storage systems,moving virtual storage systems across physical infrastructure, loadbalancing, replication policies) to the customer's or provider'stechnical personnel.

For further explanation, FIG. 20 illustrates an example virtual storagesystem 2000 architecture in accordance with some embodiments. Thevirtual storage system architecture may include similar virtualcomponents as the cloud-based virtual storage systems and on-premisesvirtual storage systems described above with reference to FIG. 4-19.

In this implementation, a virtual storage system 2000 includes aninstance of on-premises virtual storage system 2002 and an instance ofcloud-based virtual storage system 2004. In some examples, the virtualstorage system 2000 is constructed by reconstructing the on-premisesvirtual storage system 2002 in the cloud computing environment 402 tocreate the cloud-based virtual storage system 2004, for example, as partof a scale out operation or migration of a virtual storage systemdataset to the cloud computing environment 402. In some examples, thevirtual storage system 2000 is constructed by reconstructing thecloud-based virtual storage system 2004 in the virtual computingenvironment 1902 to create the on-premises virtual storage system 2002,for example, to reduce latency by moving the virtual storage systemcloser to the physical storage resources in a data center on-premises.In some examples, the on-premises virtual storage system 2002 and thecloud-based virtual storage system 2004 may be configured tosynchronously replicate data between the two virtual storage systems,such that the presented virtual storage system 2000 can continue runningand providing its services even in the event of a loss of data oravailability in either virtual storage system instance. In the exampledepicted in FIG. 20, the on-premises virtual storage system 2002 and thecloud-based virtual storage system 2004 share the cloud-based objectstorage 432 as durable back-end storage, also it will be appreciatedthat in some implementations the on-premises virtual storage system 2002and the cloud-based virtual storage system 2004 may be attached torespective object stores or respective buckets in an object store.

Consider an example where a dataset or portion thereof is migrated fromthe on-premises virtual storage system 2002 to the cloud-based virtualstorage system 2004, for example, in response to a user request ordetection of a fault. Virtual storage system logic may spin up instancesof the virtual controllers and virtual drives of the on-premises virtualstorage system 2002 in cloud computing instances of the cloud computingenvironment (e.g., by implementing a virtual controller in an AWS EC2instance and a virtual drive in an AWS EC2 instance with local instancestore). Virtual storage system logic may then migrate the data in thelocal storage and/or block storage volume of the on-premises virtualstorage system 2002 to the local storage and block storage volume of thecloud computing environment (e.g., by copying data to the AWS EC2instance with local storage and an attached EBS volume). In the event ofa fault in the on-premises virtual storage system 2002, the localstorage and block storage volume of the cloud-based virtual storagesystem 2004 may be rehydrated with data from the shared cloud-basedobject storage. Further, the virtual storage system logic may apply thesame connectivity, policies, and other configurations of the on-premisesvirtual storage system 2002 to the cloud-based virtual storage system2004. The process may be reversed, for example, by creating computeinstances in the virtual environment 1902 and migrating the virtualcontroller and virtual drives from cloud-computing instances to thecompute instances of the virtual environment 1902, and copying the datafrom the local storage and block storage of the cloud-based virtualstorage system 2004 to the on-premises virtual storage system 2002. Insome examples, the compute instances 1904, 1910 and drive instances1910-1916 may be AWS EC2 instances that are hosted in the virtualenvironment 1902 of the on-premises physical resources.

In some examples, the on-premises virtual storage system 2002 and thecloud-based virtual storage system 2004 may be configured tosynchronously replicate data between the two virtual storage systems,such that the presented virtual storage system 2000 can continue runningand providing its services even in the event of a loss of data oravailability in either virtual storage system instance. Such animplementation could be further implemented to share use of durableobjects, such that the storing of data into the object store iscoordinated so that the two virtual storage systems 2002, 2004 do notduplicate the stored content. Further, in such an implementation, thetwo synchronously replicating virtual storage systems 2002, 2004 maysynchronously replicate updates to the staging memories and perhapslocal instance stores, to greatly reduce the chance of data loss, whilecoordinating updates to object stores as a later asynchronous activityto greatly reduce the cost of capacity stored in the object store.

For further explanation, FIG. 21 sets forth a flow chart illustrating anexample method of creating a virtual storage system 2100. The examplemethod depicted in FIG. 21 may be implemented on any of the virtualstorage systems described above with reference to FIGS. 12-20. In otherwords, the example method may implement either virtual storage system1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, or 2000. Accordingly,the example method depicted in FIG. 21 may include creating acloud-based virtual storage systems, an on-premises virtual storagesystems, or combinations thereof.

In some implementations, creating virtual storage system 2100 may beperformed on a virtual platform 2130 such as the cloud computingenvironment 402 or the virtual computing environment 1902. In someexamples, creating virtual storage system 2100 may be carried out by avirtual storage system service 2110. Such a virtual storage systemservice may be defined as a service that can dynamically create virtualstorage systems within and across a variety of local and cloudplatforms, and that may be able to use a variety virtual components madeavailable in the variety of local or cloud-based platforms to providestorage services backed by multiple classes of storage, including localand cloud-based block storage, object storage, file system storage, andother classes discussed above, thereby enabling the presentation of sucha service to customers that provides choices of local and cloudplatforms and various selectable storage classes.

As depicted in FIG. 21, the example method includes instantiating (2102)one or more virtual storage controllers. Instantiating (2102) one ormore virtual storage controllers may be carried out creating the any ofthe virtual storage controllers or storage controller applicationdiscussed above with respect to the storage system architecturesdepicted in FIG. 4-20. In some examples, instantiating (2102) one ormore virtual storage controllers is carried out by creating one or morecomputing instances that host a storage controller application. In oneexample, a virtual controller may be instantiated in a cloud computingenvironment 402, using services offered by a cloud-services provider, bycreating a cloud computing instance 404, 406 to host a storagecontroller application 408, 410. In another example, a virtualcontroller may be instantiated in a virtual computing environment 1902hosted on on-premises physical resources by creating a compute instance1904, 1906 to host a storage controller application 408, 410.Instantiating (2102) one or more virtual storage controllers may becarried out via API calls, for example, to the virtual storage systemservice (2110).

The example method depicted in FIG. 21 also includes instantiating(2104) one or more virtual storage devices each including multiplestorage tiers. Instantiating (2104) one or more virtual storage deviceseach including multiple storage tiers may be carried out as discussedabove with respect to the storage system architectures depicted in FIG.4-20. In some implementations, instantiating (2104) one or more virtualstorage devices each including multiple storage tiers may be carried outby creating a one or more computing instances that host a virtualstorage device. In various examples, the virtual storage device may be avirtual drive, virtual service, or virtual blade, as discussed above,that includes attached local storage, an attached block storage volume,and an attached object storage (e.g., cloud-based object storage 432).In one example, a virtual storage device may be instantiated in a cloudcomputing environment 402, using services offered by a cloud-servicesprovider, by creating a virtual drive cloud computing instance (e.g.,virtual drives 1210-1216) or virtual blade cloud computing instance(e.g., virtual blades 1410-1416) with an attached local storage (e.g.,local storage 1220-1226) and cloud-based block storage (e.g., blockstorage volumes 1240-1246). In another example, a virtual storage devicemay be instantiated in a virtual environment 1902 hosted on on-premisesphysical resources by creating a virtual drive (e.g., virtual drives1910-1916) with an attached local storage (e.g., local storage1920-1926) and, in some implementations, block storage (e.g., blockstorage volume 1940). In these examples the virtual storage deviceprovides access to tiers and classes of storage that differ in theirbandwidth, capacity, durability, availability, and write frequency. Inone example, instantiating (2104) one or more virtual storage deviceseach including multiple storage tiers may be carried out via API calls,for example, to the virtual storage system service 2110.

The example method depicted in FIG. 21 also includes constructing (2106)a virtual storage system 2100 in which the one or more virtual storagedevices are coupled to each of the one or more virtual storagecontrollers. Constructing (2106) a virtual storage system 2100 in whichthe one or more virtual storage devices are coupled to each of the oneor more virtual storage controllers may be carried out by implementingany of the virtual storage system architectures described above. In someimplementations, constructing (2106) a virtual storage system 2100 inwhich the one or more virtual storage devices are coupled to each of theone or more virtual storage controllers is carried out by attaching thevirtual storage devices (e.g., virtual drives 1210-1216) to the virtualcontrollers (e.g., storage controller applications 408, 410) andpresenting storage services to a client host with a namespace as if thevirtual storage system were a physical storage system. In someimplementations, constructing (2106) a virtual storage system 2100 inwhich the one or more virtual storage devices are coupled to each of theone or more virtual storage controllers is further carried out byattaching a block storage volume to each virtual storage device. In someimplementations, constructing (2106) a virtual storage system 2100 inwhich the one or more virtual storage devices are coupled to each of theone or more virtual storage controllers is further carried out byattaching cloud-based object storage to each virtual storage device. Inone example, constructing (2106) a virtual storage system 2100 in whichthe one or more virtual storage devices are coupled to each of the oneor more virtual storage controllers may be carried out by API calls, forexample to the virtual storage system service 2110.

For further explanation, FIG. 22 sets forth a flow chart illustrating anadditional example method of creating a virtual storage system inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 22 is similar to the example method depicted inFIG. 21, as the example method depicted in FIG. 22 also includesinstantiating (2102) one or more virtual storage controllers;instantiating (2104) one or more virtual storage devices each includingmultiple storage tiers; and constructing (2016) a virtual storage systemin which the one or more virtual storage devices are coupled to each ofthe one or more virtual storage controllers.

The example method depicted in FIG. 22 includes migrating (2202) adataset from the virtual storage system to another virtual storagesystem, wherein at least one of the virtual storage systems is anon-premises virtual storage system utilizing on-premises physicalstorage resources. Migrating (2202) a dataset from the virtual storagesystem to another virtual storage system, wherein at least one of thevirtual storage systems is an on-premises virtual storage systemutilizing on-premises physical storage resources may be carried out asdiscussed above, for example, with respect to the example depicted inFIG. 20. In some implementations, migrating a dataset from anon-premises virtual storage system is carried out by spinning up cloudcomputing instances to replace the virtual components of the on-premisesvirtual storage system and migrating data from the on-premises virtualstorage system to the cloud-based virtual storage system by designatingthe cloud-based virtual storage system as the replication target. Insome implementations, migrating a dataset from to an on-premises virtualstorage system is carried out by spinning up computes instances in thehosted virtual environment to replace the virtual components of thecloud-based virtual storage system and migrating data from thecloud-based virtual storage system to the on-premises virtual storagesystem by designating the on-premises virtual storage system as thereplication target.

While components, data, and policies of a virtual storage system in apublic cloud infrastructure may be easily grouped together by anaccount, there is no analog for a physical on-premises storage system.Accordingly, an on-premises virtual storage system may embody anadministrative unit representing volumes, file systems, object stores,analytic stores, snapshots, policies, connectivity and otheradministrative entities that are related by the virtual storage system,where administrative changes (e.g., moving the administrative unitacross storage systems, moving the administrative unit between storageclasses) made to the administrative unit operate on each administrativeentity in the administrative unit. In some implementations, membershipin the administrative unit may be defined by a user (e.g., anadministrator) selecting or creating entities for inclusion in theadministrative unit. In some implementations, membership in theadministrative unit may be determined based on a set of affinity rules.Using a set of affinity rules, membership may be inferred based on, forexample, a commonality of stored datasets, policies, replication orsynchronization cohorts, host attachment, physical location and othershared characteristics. In some implementations, membership in theadministrative unit may be determined based on a set of anti-affinityrules. Using a set of anti-affinity rules, membership may be inferredbased on, for example, an incongruence of stored datasets, policies,replication or synchronization cohorts, host attachment, physicallocation, and other characteristics that are not shared or, in somecases, incompatible or unlikely to be shared.

Readers will appreciate that migrating a dataset from an on-premisesvirtual storage system to another virtual or physical storage systemincludes migrating policies, metadata, and connectivity for the dataset,such that the virtual storage system may be reconstructed in thereplication target.

For further explanation, FIG. 23 sets forth a flow chart illustrating anadditional example method of creating a virtual storage system inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 23 is similar to the example method depicted inFIG. 21, as the example method depicted in FIG. 23 also includesinstantiating (2102) one or more virtual storage controllers;instantiating (2104) one or more virtual storage devices each includingmultiple storage tiers; and constructing (2016) a virtual storage systemin which the one or more virtual storage devices are coupled to each ofthe one or more virtual storage controllers.

The example method depicted in FIG. 23 also includes migrating (2302) adataset from an on-premises virtual storage system to a nativeenvironment executing on a physical storage system. Migrating (2302) adataset from an on-premises virtual storage system to a nativeenvironment executing on a physical storage system may be carried out byreconstructing the on-premises virtual storage system (e.g., theon-premises virtual storage system 1900 depicted in FIG. 19) usingstorage controller resources native to the physical storage system andstorage resources available in the physical storage system (e.g.,storage systems depicted in FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B); andmigrating data in the virtual storage system to the physical storagesystem. In some implementations, the virtual storage controller mayimplement the same storage controller application that is hosted in thephysical storage environment. In this example, a storage controllerapplication of the native operating environment, such as a storageoperating system, may be configured as the storage controller of themigration target. In one example, a virtual storage device in thevirtual storage system, characterized by performance, capacity,durability, and other metrics, is approximated using physical storageresources available in the physical environment. In this example, theconnectivity among the storage controllers and physical storageresources is also recreated. For example, the virtual storage system mayoperate as a testing and development model before deploying the storagesystem in a physical storage array.

For further explanation, FIG. 24 sets forth a flow chart illustrating anadditional example method of creating a virtual storage system inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 24 is similar to the example method depicted inFIG. 21, as the example method depicted in FIG. 24 also includesinstantiating (2102) one or more virtual storage controllers;instantiating (2104) one or more virtual storage devices each includingmultiple storage tiers; and constructing (2016) a virtual storage systemin which the one or more virtual storage devices are coupled to each ofthe one or more virtual storage controllers.

The example method depicted in FIG. 24 also includes exposing (2404) afirst set of interfaces to a first role for the virtual storage system,wherein the first set of interfaces configure a physical environmenthosting the virtual storage system. In some examples, exposing (2404) afirst set of interfaces to a first role in the virtual storage system,wherein the second set of interfaces configure a physical environmenthosting the virtual storage system, is carried out by exposing APIs forprovisioning a virtual storage system on physical storage resources suchthat an infrastructure administrator may access these APIs. In theseexamples, an infrastructure administrator role administers the physicalstorage environment that supports the virtual storage system, such ascapacities for virtual storage system consumption; hardware failures;network connectivity and topology; protection policies (e.g., snapshotand replication policies); load balancing virtual storage systemmovement across a physical infrastructure; physical system wideperformance reserves and trends; physical system wide storage capacitiesand reserves; replication state to other zones, and virtual storagesystem-level replication and protection policies. The infrastructureadministrator is responsible for creating and mapping virtual storagesystem administrators to virtual storage systems, defining virtualstorage system quotas and other virtual storage system-level limits,cloning virtual storage systems to new virtual storage systems; creatingdeduplication reports across virtual storage system, arrays, and zone.

In the example of FIG. 24, a virtual storage system infrastructure mayexpose services and APIs for creating and managing the infrastructuresupporting the virtual storage system. In one implementation, a set ofAPIs available to infrastructure administrator may include interfaces tocreate virtual storage system; define administrators for virtual storagesystem; define replication relationship for virtual storage system;increase a logical quota to virtual storage system, recover virtualstorage system from a snapshot; create a replication link for a volume;move a volume between virtual storage system; set QoS targets for avirtual storage system; query volume size and deduplication efficiency.

The example method depicted in FIG. 24 also includes exposing (2402) asecond set of interfaces to a second role for the virtual storagesystem, wherein the second set of interfaces configure virtualcomponents in the virtual storage system. In some examples, exposing(2402) a second set of interfaces to a second role for the virtualstorage system, wherein the second set of interfaces configure virtualcomponents in the virtual storage system, is carried out by exposingvirtual computing environment APIs or cloud computing environment APIssuch that a system administrator of the virtual storage system mayaccess these APIs to configure virtual components. In these examples,the system administrator role configures and maintains the virtualstorage system for use by an application, including tasks such as suchmanaging the virtual storage system dataset including creating volumes,buckets, file systems, and other addressable portions of memory withinthe virtual storage system; attaching logical limits (e.g., quotas orsizes) virtual storage system components; defining access control listsfor virtual storage resources; monitoring logical space consumption ofthe virtual storage system and its components; monitoring theperformance of the virtual storage system and its performance;monitoring connectivity performance within the virtual storage systemand to external application hosts; defining snapshot policies.

In the example of FIG. 24, a virtual storage system may expose servicesand APIs for configuring an administering the virtual storage system. Inone implementation, a set of APIs available to the system administratormay include interfaces to provision a volume; take snapshot of a virtualstorage system, dataset, or volume; connect to a host; create a virtualstorage system snapshot schedule; manually delete snapshots; create amanual snapshots; add a host to a connection policy; drop a volume; growa volume; shrink a volume; recover a volume from a snapshot; set QoStargets for a volume; query volume connectivity state; query volumeperformance statistics; query volume access points.

Readers will appreciate that the management of the virtual storagesystem may be separately performed by the system administrator role andthe infrastructure administrator role, such that the systemadministrator role is provided with interfaces to configure the virtualstorage system without configuring the underlying infrastructure or evenany knowledge of the physical hardware. This aspect is particularlyadvantageous with respect to on-premises virtual storage systems, wherethe system administrator of the virtual storage system administers astorage services that are abstracted from the physical hardware. Unlikecloud services provided by a cloud services provider, where the consumerof storage resources cannot configure the hardware infrastructure, in anon-premises virtual storage system the underlying physicalinfrastructure is configured to host the virtual storage system by, forexample, an infrastructure administrator.

Readers will appreciate that, whereas the on-premises virtual storagesystem must be provisioned on the on-premises hardware resources basedon some awareness of the available resources, a system administrator ofthe virtual storage system may configure storage services and storagecomponents without this awareness. In this way, the system administratorof the virtual storage system may administer storage systems usinginfrastructure-agnostic interfaces such as those provided in acloud-based platform; for example, by selecting configurations andpolicies for the virtual storage system based on desired performance(e.g., bandwidth, capacity), reliability (e.g., durability,availability), and data characteristics. In some implementations, set ofAPIs for configuring an maintaining the on-premises virtual storagesystem are exclusively available to the system administrator role,whereas the set of APIs for configuring and administering theinfrastructure supporting the on-premises virtual storage system areexclusively available to the infrastructure administrator role, suchthat the system administrator role is distinct from the infrastructureadministrator role.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

Statement 1. A method of servicing I/O operations in a virtual storagesystem, the method comprising: receiving, by the virtual storage system,a request to write data to the virtual storage system; storing, withinstorage provided by a first tier of storage of the virtual storagesystem, the data; and migrating, from the first tier of storage to asecond tier of storage that is more durable than the first tier ofstorage of the virtual storage system, at least a portion of data storedwithin the first tier of storage.

Statement 2. The method of statement 1, wherein migrating the at leastthe portion of data stored within the staging memory is responsive todetecting a condition for transferring data between the staging memoryto the durable data storage provided by the cloud services provider.

Statement 3. The method of statement 2 or statement 1, wherein thestaging memory includes multiple virtual drive servers.

Statement 4. The method of statement 3, statement 2, or statement 1,wherein the multiple virtual drive servers include respective localstorage.

Statement 5. The method of statement 4, statement 3, statement 2, orstatement 1, wherein the multiple virtual drive servers provide virtualdrives as block-type data storage.

Statement 6. The method of statement 5, statement 4, statement 3,statement 2, or statement 1, wherein the request to write data to thevirtual storage system is received by one or more virtual controllersrunning within a virtual machine, a container, or a bare metal server.

Statement 7. The method of statement 6, statement 5, statement 4,statement 3, statement 2, or statement 1, wherein staging memory isprovided by multiple virtual drive servers that respectively include aboth virtual controller and local memory.

Statement 8. The method of statement 7, statement 6, statement 5,statement 4, statement 3, statement 2, or statement 1, wherein the atleast the portion of the data stored within the staging memory isdeduplicated, encrypted, or compressed prior to migration from thestaging memory to the durable data storage.

Statement 9. The method of statement 8, statement 7, statement 6,statement 5, statement 4, statement 3, statement 2, or statement 1,wherein the staging memory of the virtual storage system ischaracterized by a low read latency relative to the durable data storageprovided by the cloud services provider.

Statement 10. The method of statement 9, statement 8, statement 7,statement 6, statement 5, statement 4, statement 3, statement 2, orstatement 1, wherein the first tier of storage includes staging memorythat provides transactional consistency and write acknowledgments, andwherein the second tier of storage includes virtual drives provided byvirtual drive servers of the virtual storage system.

Statement 11. The method of statement 10, statement 9, statement 8,statement 7, statement 6, statement 5, statement 4, statement 3,statement 2, or statement 1, wherein the first tier of storage includesthe virtual drives provided by virtual drive servers of the virtualstorage system, and wherein the second tier includes object storageprovided by a cloud services provider that provides object storageindependent of the virtual storage system.

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

Statement 1. A virtual storage system contained in a cloud computingenvironment, the cloud-based storage system including: one or morevirtual drives providing a staging memory for storage operations; andone or more virtual controllers, each virtual controller executing in acloud computing instance, wherein the one or more virtual controllersare configured to: receive, by the virtual storage system, a request towrite data to the virtual storage system; store, within storage providedby a first tier of storage of the virtual storage system, the data; andmigrate, from the first tier of storage to a second tier of storage thatis more durable than the first tier of storage of the virtual storagesystem, at least a portion of data stored within the staging memory.

Statement 2. The virtual storage system of statement 1, wherein localstorage for a given virtual drive is connected to one or more othervirtual drives.

Statement 3. The virtual storage system of statement 2 or statement 1,wherein a first subset of virtual controllers is located within a firstavailability zone, and wherein a second subset of virtual controllers islocated within a second availability zone.

Statement 4. The virtual storage system of statement 3, statement 2, orstatement 1, wherein a first subset of virtual drives is located withina first availability zone, and wherein a second subset of virtualdrivers is located within a second availability zone.

Statement 5. The virtual storage system of statement 4, statement 3,statement 2, or statement 1, wherein both the first subset of virtualdrives in the first availability zone and the second subset of virtualdrives in the second availability zone use a same cloud-based objectstorage.

Statement 6. The virtual storage system of statement 5, statement 4,statement 3, statement 2, or statement 1, wherein migrating the at leastthe portion of data stored within the staging memory is responsive todetecting a condition for transferring data between the staging memoryto the durable data storage provided by the cloud services provider.

Statement 7. The virtual storage system of statement 6, statement 5,statement 4, statement 3, statement 2, or statement 1, wherein thestaging memory includes multiple virtual drive servers.

Statement 8. The virtual storage system of statement 7, statement 6,statement 5, statement 4, statement 3, statement 2, or statement 1,wherein the respective virtual drives include both a respective virtualcontroller and respective local storage.

Statement 9. The virtual storage system of statement 8, statement 7,statement 6, statement 5, statement 4, statement 3, statement 2, orstatement 1, wherein staging memory is provided by multiple virtualdrive servers that respectively include a both virtual controller andlocal memory.

Statement 10. The virtual storage system of statement 9, statement 8,statement 7, statement 6, statement 5, statement 4, statement 3,statement 2, or statement 1, wherein the virtual storage systemsynchronously replicates the data with one or more other virtual storagesystems.

Statement 11. The virtual storage system of statement 10, statement 9,statement 8, statement 7, statement 6, statement 5, statement 4,statement 3, statement 2, or statement 1, wherein a virtual storagesystem architecture implementing the virtual storage system is distinctfrom a virtual storage system architecture implementing at least onevirtual storage system among the one or more other virtual storagesystems.

What is claimed is:
 1. A method comprising: constructing a virtualstorage system in which the one or more virtual storage devices arecoupled to each of one or more virtual storage controllers; andreplicating a dataset from the virtual storage system to another virtualstorage system, wherein at least one of the virtual storage systems isan on-premises virtual storage system utilizing on-premises physicalstorage resources.
 2. The method of claim 1, wherein the one or morevirtual storage devices each include local storage.
 3. The method ofclaim 1, wherein the one or more virtual storage devices are attached tocloud-based object storage.
 4. The method of claim 1, wherein thevirtual storage system is cloud-based virtual storage system createdusing services offered by a cloud services provider.
 5. The method ofclaim 4, wherein the one or more virtual controllers are implemented inrespective cloud computing instances of a cloud platform; and whereinthe one or more virtual storage devices are implemented in respectivecloud computing instances of the cloud platform.
 6. The method of claim1, wherein the virtual storage system is an on-premises virtual storagesystem created a virtual environment supported by on-premises physicalstorage resources.
 7. The method of claim 1 further comprising migratinga dataset from the virtual storage system to another virtual storagesystem, wherein at least one of the virtual storage systems is anon-premises virtual storage system utilizing on-premises physicalstorage resources.
 8. The method of claim 1 further comprising migratinga dataset from an on-premises virtual storage system to a nativeenvironment executing on a physical storage system.
 9. The method ofclaim 1 further comprising: instantiating the one or more virtualstorage controllers; and instantiating the one or more virtual storagedevices each including multiple storage tiers.
 10. The method of claim 1further comprising migrating a dataset from an on-premises virtualstorage system to a native environment executing on a physical storagesystem.
 11. The method of claim 1 further comprising: exposing a firstset of interfaces to a first role for the virtual storage system,wherein the first set of interfaces configure a physical environmenthosting the virtual storage system; and exposing a second set ofinterfaces to a second role for the virtual storage system, wherein thesecond set of interfaces configure virtual components in the virtualstorage system.
 12. An apparatus comprising a computer processor, acomputer memory operatively coupled to the computer processor, thecomputer memory having disposed within it computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: constructing a virtual storage system in whichthe one or more virtual storage devices are coupled to each of one ormore virtual storage controllers; and replicating a dataset from thevirtual storage system to another virtual storage system, wherein atleast one of the virtual storage systems is an on-premises virtualstorage system utilizing on-premises physical storage resources.
 13. Theapparatus of claim 12 further comprising computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of migrating a dataset from the virtual storagesystem to another virtual storage system, wherein at least one of thevirtual storage systems is an on-premises virtual storage systemutilizing on-premises physical storage resources.
 14. The apparatus ofclaim 12 further comprising computer program instructions that, whenexecuted by the computer processor, cause the apparatus to carry out thesteps of migrating a dataset from an on-premises virtual storage systemto a native environment executing on a physical storage system.
 15. Theapparatus of claim 12 further comprising computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: instantiating the one or more virtual storagecontrollers; and instantiating the one or more virtual storage deviceseach including multiple storage tiers.
 16. The apparatus of claim 12further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps ofreplicating a dataset from an on-premises virtual storage system to anative environment executing on a physical storage system.
 17. Theapparatus of claim 12 further comprising computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: exposing a first set of interfaces to a firstrole for the virtual storage system, wherein the first set of interfacesconfigure a physical environment hosting the virtual storage system; andexposing a second set of interfaces to a second role for the virtualstorage system, wherein the second set of interfaces configure virtualcomponents in the virtual storage system.
 18. A computer program productdisposed upon a computer readable medium, the computer program productcomprising computer program instructions that, when executed, cause acomputer to carry out the steps of: constructing a virtual storagesystem in which the one or more virtual storage devices are coupled toeach of one or more virtual storage controllers; and replicating adataset from the virtual storage system to another virtual storagesystem, wherein at least one of the virtual storage systems is anon-premises virtual storage system utilizing on-premises physicalstorage resources.
 19. The computer program product of claim 18 furthercomprising computer program instructions that, when executed, cause thecomputer to carry out the steps of migrating a dataset from the virtualstorage system to another virtual storage system, wherein at least oneof the virtual storage systems is an on-premises virtual storage systemutilizing on-premises physical storage resources.
 20. The computerprogram product of claim 18 further comprising computer programinstructions that, when executed, cause the computer to carry out thesteps of migrating a dataset from an on-premises virtual storage systemto a native environment executing on a physical storage system.